8. Kosonen, H., Heinonen, M., Mikola, A., Haimi, H., Mulas, M., Corona, F., and Vahala, R., Nitrous Oxide Production at a Fully Covered Wastewater Treatment Plant: Results of a Long-Term Online Monitoring Campaign. Environmental Science & Technology, Vol. 50, 2016, pp. 5547-5554.

7. Mulas, M., Corona, F., Sirviö, J., Hyvönen, S., and Vahala, R., Full-scale implementation of an advanced control system on a biological wastewater treatment plant. International Federation of Automatic Control (IFAC), IFAC-PapersOnLine 49-7, 2016, pp. 1163-1168.

6. Haimi, H., Mulas, M., Corona, F., Marsili-Libelli, S., Lindell, P., Heinonen, M., and Vahala, R., Adaptive data-derived anomaly detection in the activated sludge process of large-scale wastewater treatment plant. Engineering Applications of Artificial Intelligence, Vol. 52, 2016, pp. 65-80.

5. Zhao, C., van Heeswijk, M., and Karhunen, J., Air Quality Forecasting Using Neural Networks. In Proc. of The IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), Athens, Greece, December 2016. 7 pages.

4. Android Malware Detection: Building Useful Representations

Luiza Sayfullina, Emil Eirola, Dmitry Komashinsky, Paolo Palumbo and Juha Karhunen.
   In Proc. of The 15th IEEE Int. Conf. on Machine Learning and Applications (ICMLA 2016), Anaheim, California, USA, December 2016.

3. Gaussian Process Kernels for Popular State-Space Time Series Models

Alexander Grigorievskiy and Juha Karhunen.
   In Proc. of The Int. Joint. Conf. on Neural Networks (IJCNN 2016), Vancouver, Canada, July 2016.

2. Probabilistic Methods for Multiclass Classification Problems

Andrey Gritsenko, Emil Eirola, Daniel Schupp, Edward Ratner and Amaury Lendasse.
   In Proceedings of ELM-2015 Volume 2, volume 7, pages 385-397. 2016.

1. Extreme Learning Machine for Missing Data using Multiple Imputations

Dušan Sovilj, Emil Eirola, Yoan Miche, Kaj-Mikael Bjork, Rui Nian, Anton Akusok and Amaury Lendasse.
   In Neurocomputing, volume 174, Part A, pages 220 - 231. 2016.

20. Statistical Study of Strong and Extreme Geomagnetic Disturbances and Solar Cycle Characteristics

E. K. J. Kilpua, Nigul Olspert, Alexander Grigorievskiy, Maarit Mantere, E. I. Tanskanen, H. Miyahara, R. Kataoka, Jaan Pelt and Y. D. Liu.
   In The Astrophysical Journal, volume 806. 2015.

19. Minimal Learning Machine: A novel supervised distance-based approach for regression and classification

Amauri Holanda de Souza Junior, Guilherme Barreto and Francesco Corona.
   In Neurocomputing, volume 147, pages 34-44. 2015.

18. An application of predictive control to the Viikinmaki wastewater treatment plant

Michela Mulas, Stefania Tronci, Francesco Corona, Henri Haimi, Paula Lindell, Mari Heinonen, Riku Vahala and Roberto Baratti.
   In Journal of Process Control, volume 35, pages 89 - 100. 2015.

17. Efficient detection of zero-day Android Malware using Normalized Bernoulli Naive Bayes

Luiza Sayfullina, Emil Eirola, Dmitry Komashinsky, Paolo Palumbo, Yoan Miche, Amaury Lendasse and Juha Karhunen.
   In Trustcom/BigDataSE/ISPA, 2015 IEEE, volume 1, pages 198-205. 2015.

16. Shall we use hardware sensor measurements or soft-sensor estimates? Case study in a full-scale WWTP

Henri Haimi, Francesco Corona, Michela Mulas, Laura Sundell, Mari Heinonen and Riku Vahala.
   In Environmental Modeling & Software, volume 72, pages 215-229. 2015.

15. Extreme Learning Machines for Multiclass Classification: Refining Predictions with Gaussian Mixture Models

Emil Eirola, Andrey Gritsenko, Anton Akusok, Kaj-Mikael Bjork, Yoan Miche, Dušan Sovilj, Rui Nian, Bo He and Amaury Lendasse.
   In Advances in Computational Intelligence - 13th International Work-Conference on Artificial Neural Networks, IWANN 2015, Palma de Mallorca, Spain, June 10-12, 2015. Proceedings, Part II, volume 9095, pages 153-164. 2015.

14. Stochastic Discriminant Analysis

Mika Juuti, Francesco Corona and Juha Karhunen.
   In International Joint Conference on Neural Networks (IJCNN 2015). 2015.

13. Brain MRI Morphological Patterns Extraction Tool based on Extreme Learning Machine and Majority Vote Classification

Maite Termenon, Manuel Grana, Alexandre Savio, Anton Akusok, Yoan Miche and Amaury Lendasse.
   In Neurocomputing. 2015, in press. Available online May 2015..

12. Reducing sparsity for Text Classification

Luiza Sayfullina, Yoan Miche and Emil Eirola.
   In International Conference on Computational Social Science. 2015, Accepted..

11. Singular Value Decomposition Update and Its Application to (Inc)-OP-ELM

Alexander Grigorievskiy, Yoan Miche, Maarit Mantere and Amaury Lendasse.
   In Neurocomputing. 2015, Accepted..

10. Accelerated isotope fine structure calculation using pruned transition trees

Martin Loos, Christian Gerber, Francesco Corona, Juliane Hollender and Heinz Singer.
   In Analytical Chemistry, volume 87, pages 5738-5744. 2015.

9. Arbitrary Category Classification of Websites Based on Image Content

Anton Akusok, Yoan Miche, Juha Karhunen, Kaj-Mikael Bjork, Rui Nian and Amaury Lendasse.
   In IEEE CIM Magazine. 2015, in press. Available online April 2015..

8. Towards a Tomographic Index of Systemic Risk Measures

Kaj-Mikael Bjork, Patrick Kouontchou, Amaury Lendasse, Yoan Miche and Bertrand Maillet.
   In ESANN 2015. 2015.

7. Advances in Extreme Learning Machines

Mark van Heeswijk.
April, 2015, PhD Thesis.

6. Scenario simulations of future salinity and ecological consequences in the Baltic Sea and adjacent North Sea areas - Implications for environmental monitoring

Ilppo Vuorinen, Jari Hänninen, Marjut Rajasilta, Paivi Laine, Jan Eklund, Federico Montesino Pouzols, Francesco Corona, Karin Junker, H.E. Markus Meier and Joachim Dippner.
   In Ecological Indicators, volume 50, pages 196-205. 2015.

5. MD-ELM: Originally Mislabeled Samples Detection using OP-ELM Model

Anton Akusok, David Veganzones, Yoan Miche, Kaj-Mikael Bjork, Eric Séverin and Amaury Lendasse.
   In Neurocomputing. 2015, in press. Available online..

4. SOM-ELM: Self-Organized Clustering Using ELM

Yoan Miche, Anton Akusok, David Veganzones, Kaj-Mikael Bjork, Eric Séverin, Philippe duJardin, Maite Termenon and Amaury Lendasse.
   In Neurocomputing. 2015, in press. Available online..

3. Regional models: A new approach for nonlinear system identification via clustering of the self-organizing map

Amauri Holanda de Souza Junior, Guilherme Barreto and Francesco Corona.
   In Neurocomputing, volume 147, pages 31-46. 2015.

2. Supervised Distance Preserving Projections: Applications in the quantitative analysis of diesel fuels and light cycle oils

Francesco Corona, Zhanxing Zhu, Amauri Holanda de Souza Junior, Michela Mulas, Emanuela Muru, Lorenzo Sassu, Guilherme Barreto and Roberto Baratti.
   In Journal of Process Control, volume 30, pages 10-21. 2015.

1. Binary/Ternary Extreme Learning Machines

Mark van Heeswijk and Yoan Miche.
   In Neurocomputing, volume 149, pages 187-197. February, 2015.

15. Machine learning methods for incomplete data and variable selection

Emil Eirola.
October, 2014.

14. Supervised Probability Preserving Projection (SPPP)

Srikrishna Raamadhurai.
October, 2014, Master's Thesis.

13. Reducing Sparsity in Sentiment Analysis Data using Novel Dimensionality Reduction Approaches

Luiza Sayfullina.
October, 2014, Master's Thesis.

12. Compressive ELM: Improved Models through Exploiting Time-Accuracy Trade-Offs

Mark van Heeswijk, Amaury Lendasse and Yoan Miche.
   In Engineering Applications of Neural Networks, volume 459, pages 165-174. 2014.

11. HP Trend Filtering Using Gaussian Mixture Model Weighted Heuristic

Luiza Sayfullina, Magnus Westerlund, Kaj-Mikael Bjork and Hannu Toivonen.
2014.

10. Variable Selection for Regression Problems Using Gaussian Mixture Models to Estimate Mutual Information

Emil Eirola, Amaury Lendasse and Juha Karhunen.
   In International Joint Conference on Neural Networks (IJCNN 2014), pages 1606-1613. July, 2014.

9. The Delta Test: The 1-NN Estimator as a Feature Selection Criterion

Emil Eirola, Amaury Lendasse, Francesco Corona and Michel Verleysen.
   In International Joint Conference on Neural Networks (IJCNN 2014), pages 4214-4222. July, 2014.

8. Forecasting the Outbursts of the Photometry Light Curve of Star V363 Lyr

Alexander Grigorievskiy, Maarit Mantere, Anton Akusok, Emil Eirola and Amaury Lendasse.
   In International Work Conference on Time Series Analysis, volume 1, pages 520-531. 2014.

7. Finding Originally Mislabels with MD-ELM

Anton Akusok, David Veganzones, Yoan Miche, Eric Séverin and Amaury Lendasse.
   In ESANN 2014: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 2014.

6. Fast Feature Selection in a GPU Cluster Using the Delta Test

Alberto Guillén, Maribel García Arenas, Mark van Heeswijk, Dušan Sovilj, Amaury Lendasse, Luis Herrera, Hector Pomares and Ignacio Rojas.
   In Entropy, volume 16, pages 854-869. February, 2014.

5. Mixture of Gaussians for distance estimation with missing data

Emil Eirola, Amaury Lendasse, Vincent Vandewalle and Christophe Biernacki.
   In Neurocomputing, volume 131, pages 32--42. 2014.

4. Extreme Learning Machine towards Dynamic Model Hypothesis in Fish Ethology Research

Rui Nian, Bo He, Bing Zheng, Mark van Heeswijk, Qi Yu, Yoan Miche and Amaury Lendasse.
   In Neurocomputing, volume 128, pages 273-284. March, 2014.

3. Ensemble Delta Test- Extreme Learning Machine (DT-ELM) For Regression

Qi Yu, Mark van Heeswijk, Yoan Miche, Rui Nian, Bo He, Eric Séverin and Amaury Lendasse.
   In Neurocomputing, volume 129, pages 153-158. April, 2014.

2. Fast Face Recognition Via Sparse Coding and Extreme Learning Machine

Bo He, Dongxun Xu, Rui Nian, Mark van Heeswijk, Qi Yu, Yoan Miche and Amaury Lendasse.
   In Cognitive Computation, volume 6, pages 264-277. June, 2014.

1. Long-term Time Series Prediction using OP-ELM

Alexander Grigorievskiy, Yoan Miche, Anne-Mari Ventelä, Eric Séverin and Amaury Lendasse.
   In Neural Networks, volume 51, pages 50-56. 2014.

13. ELMVIS: a Nonlinear Visualization Technique using Random Permutations and Extreme Learning Machine

Anton Akusok, Amaury Lendasse, Francesco Corona, Rui Nian and Yoan Miche.
   In IEEE Transactions on Intelligent Systems, volume 28, pages 41-46. December, 2013.

12. A neural network algorithm for cloud fraction estimation using NASA-Aura OMI VIS radiance measurements

G. Saponaro, P. Kolmonen, Juha Karhunen, J. Tamminen and G. de Leeuw.
   In Atmospheric Measurement Techniques, volume 6, pages 2301--2309. 2013.

11. Gaussian Mixture Models for Time Series Modelling, Forecasting, and Interpolation

Emil Eirola and Amaury Lendasse.
   In Advances in Intelligent Data Analysis XII 12th International Symposium, IDA 2013, London, UK, October 17-19, 2013, volume 8207, pages 162-173. 2013.

10. Practical Estimation of Missing Phosphorus Values in Pyhajarvi Lake Data

Alexander Grigorievskiy, Anton Akusok, Marjo Tarvainen, Anne-Mari Ventelä and Amaury Lendasse.
   In Machine Learning Reports 02/2013, volume ISSN: 1865-3960, pages 8-16. September, 2013, Proceedings of the Workshop - New Challenges in Neural Computation 2013.

9. Image-based Classification of Websites

Anton Akusok, Alexander Grigorievskiy, Amaury Lendasse and Yoan Miche.
   In Machine Learning Reports 02/2013, volume ISSN: 1865-3960, pages 25-34. September, 2013, Proceedings of the Workshop - New Challenges in Neural Computation 2013.

8. Distance Estimation in Numerical Data Sets with Missing Values

Emil Eirola, Gauthier Doquire, Michel Verleysen and Amaury Lendasse.
   In Information Sciences, volume 240, pages 115--128. 2013.

7. Missing Values Estimation: The Pyhäjärvi Case

Alexander Grigorievskiy.
2013, Master's Thesis. Grade: 5/5 "with distinction".

6. Extending Extreme Learning Machine with Combination Layer

Dušan Sovilj, Amaury Lendasse and Olli Simula.
   In IWANN 2013, Part I, volume 7902, pages 417-426. June 12-14, 2013.

5. Meme Representations for Game Agents

Yoan Miche, Meng-Hiot Lim, Amaury Lendasse and Yew-Soon Ong.
   In World Wide Web, pages 1--20. 2013.

4. A Two-Stage Methodology using K-NN and False Positive Minimizing ELM for Nominal Data Classification

Anton Akusok, Yoan Miche, József Hegedüs, Rui Nian and Amaury Lendasse.
   In Cognitive Computation. 2013, to appear.

3. Bankruptcy Prediction using Extreme Learning Machine and Financial Expertise

Qi Yu, Yoan Miche, Eric Séverin and Amaury Lendasse.
   In Neurocomputing. 2013, to appear.

2. Feature Selection for Nonlinear Models using Extreme Learning Machines

Benoît Frénay, Mark van Heeswijk, Yoan Miche, Michel Verleysen and Amaury Lendasse.
   In Neurocomputing, volume 102, pages 111-124. 2013.

1. Regularized Extreme Learning Machine For Regression with Missing Data

Qi Yu, Yoan Miche, Emil Eirola, Mark van Heeswijk, Eric Séverin and Amaury Lendasse.
   In Neurocomputing, volume 102, pages 45–51. 2013.

5. Mixture of Gaussians for Distance Estimation with Missing Data

Emil Eirola, Amaury Lendasse, Vincent Vandewalle and Christophe Biernacki.
   In Machine Learning Reports 03/2012, pages 37-45. 2012, Proceedings of the Workshop - New Challenges in Neural Computation 2012.

4. Evolutive Approaches for Variable Selection Using a Non-parametric Noise Estimator

Alberto Guillén, Dušan Sovilj, Mark van Heeswijk, Luis Javier Herrera, Amaury Lendasse, Hector Pomares and Ignacio Rojas.
Studies in Computational Intelligence, , volume 415, pages 243-266 2012.

3. Fast variable selection for memetracker phrases time series prediction

Yoan Miche, Tatiana Chistiakova, Anton Akusok, Amaury Lendasse, Rui Nian and Alberto Guillén.
   In Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments, pages 47:1--47:6. 2012.

2. Relevance learning for time series inspection

Andrej Gisbrecht, Dušan Sovilj, Barbara Hammer and Amaury Lendasse.
   In ESANN'12, pages 489-494. April 25-27, 2012.

1. Climate induced changes in benthic macrofauna -- a non-linear model approach

Karin Junker, Dušan Sovilj, Ingrid Kröncke and Joachim Dippner.
   In Journal of Marine Systems, volume 96-97, pages 90-94. August, 2012.

19. Methodology for Behavioral-based Malware Analysis and Detection using Random Projections and K-Nearest Neighbors Classifiers

József Hegedüs, Yoan Miche, Alexander Ilin and Amaury Lendasse.
   In 7th International Conference on Computational Intelligence and Security (CIS2011). December, 2011.

18. Ensembles of Local Linear Models for Bankruptcy Analysis and Prediction

Laura Kainulainen, Yoan Miche, Emil Eirola, Qi Yu, Benoît Frénay, Eric Séverin and Amaury Lendasse.
   In Case Studies in Business, Industry and Government Statistics (CSBIGS), volume 4. November, 2011.

17. Random Projection Method for Scalable Malware Classification

József Hegedüs, Yoan Miche, Alexander Ilin and Amaury Lendasse.
   In 14th International Symposium on Recent Advances in Intrusion Detection. September, 2011.

16. Local linear regression for soft-sensor design with application to an industrial deethanizer

Zhanxing Zhu, Francesco Corona, Amaury Lendasse, Roberto Baratti and Jose Romagnoli.
   In 18th World Congress of the International Federation of Automatic Control (IFAC). August, 2011.

15. Estimating Nitrate Concentration in the Post-Denitrification Unit of a Municipal Wastewater Treatment Plant

Michela Mulas, Francesco Corona, Henri Haimi, Laura Sundell, Mari Heinonen and Riku Vahala.
   In Proceedings of the 18th IFAC World Congress, volume 18, pages 6212--6217. 2011.

14. Local Linear Regression for Soft-Sensor Design with Application to an Industrial Deethanizer

Zhanxing Zhu, Francesco Corona, Amaury Lendasse, Roberto Baratti and Jose Romagnoli.
   In Proceedings of the 18th IFAC World Congress, volume 18, pages 2839--2844. 2011.

13. Bankruptcy Prediction with Missing Data

Qi Yu, Yoan Miche, Eric Séverin and Amaury Lendasse.
   In Proceedings of the 2011 International Conference on Data Mining, pages 279-285. July, 2011.

12. Nitrate Estimation in a Denitrifying post-filtration Unit of a Municipal Wastewater Treatment Plant: The Viikinmäki Case

Michela Mulas, Francesco Corona, Henri Haimi, Laura Sundell, Mari Heinonen and Riku Vahala.
   In Proceedings of the IWA-Watermatex 2011 Eighth Symposium on System Analysis and Integrated Assessment, pages 444--451. 2011.

11. Outlier Detection for the Denitrifying Post-Filtration Unit of a Municipal Wastewater Treatment Plant: The Viikinmäki Case

Henri Haimi, Francesco Corona, Michela Mulas, Laura Sundell, Mari Heinonen and Riku Vahala.
   In Proceedings of the IWA-Watermatex 2011 Eighth Symposium on System Analysis and Integrated Assessment, pages 793--800. 2011.

10. Variable Selection in a GPU Cluster Using Delta Test

Alberto Guillén, Mark van Heeswijk, Dušan Sovilj, Maribel García Arenas, Luis Javier Herrera, Hector Pomares and Ignacio Rojas.
   In IWANN (1), pages 393-400. 2011.

9. Multistart Strategy using Delta Test for Variable Selection

Dušan Sovilj.
   In ICANN 2011, volume 6792, pages 413--420. June, 2011.

8. Adaptive Kernel Smoothing Regression for Spatio-Temporal Environmental Datasets

Federico Montesino Pouzols and Amaury Lendasse.
   In ESANN 2011 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pages 87--92. 2011.

7. Locating Anomalies Using Bayesian Factorizations and Masks

Li Yao, Amaury Lendasse and Francesco Corona.
   In ESANN 2011 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pages 207--212. 2011.

6. Mining and Control of Network Traffic by Computational Intelligence

Federico Montesino Pouzols, Diego R. Lopez and Angel Barriga Barros.
Springer, volume 342. January, 2011, To Appear.

5. On the Curse of Dimensionality in Supervised Learning of Smooth Regression Functions

Elia Liitiäinen, Francesco Corona and Amaury Lendasse.
   In Neural Processing Letters, volume 34, pages 133--154. 2011.

4. Parameter-insensitive kernel in extreme learning for non-linear support vector regression

Benoît Frénay and Michel Verleysen.
   In Neurocomputing, volume 74, pages 2526 - 2531. 2011, Selected papers of the 10th International Work-Conference on Artificial Neural Networks (IWANN2009).

3. GPU-Accelerated and Parallelized ELM Ensembles for Large-scale Regression

Mark van Heeswijk, Yoan Miche, Erkki Oja and Amaury Lendasse.
   In Neurocomputing, volume 74, pages 2430-2437. September, 2011.

2. TROP-ELM: a Double-Regularized ELM using LARS and Tikhonov Regularization

Yoan Miche, Mark van Heeswijk, Patrick Bas, Olli Simula and Amaury Lendasse.
   In Neurocomputing, volume 74, pages 2413-2421. September, 2011.

1. Climate-related challenges in long-term management of Säkylän Pyhäjärvi (SW Finland)

Anne-Mari Ventelä, Teija Kirkkala, Amaury Lendasse, Marjo Tarvainen, Harri Helminen and Jouko Sarvala.
   In Hydrobiologia, volume 660, pages 49--58. 2011.

34. Methodologies for Time Series Prediction and Missing Value Imputation

Antti Sorjamaa.
November, 2010.

33. Developing Fast Machine Learning Techniques with Applications to Steganalysis Problems

Yoan Miche.
November, 2010.

32. Anomaly Detection and Location with Application to an Energy Management System

Li Yao.
2010, Master Thesis.

31. Interpreting Extreme Learning Machine as an Approximation to an Infinite Neural Network

Elina Parviainen, Jaakko Riihimäki, Yoan Miche and Amaury Lendasse.
   In KDIR 2010: Proceedings of the International Conference on Knowledge Discovery and Information Retrieval . October, 2010.

30. Automatic Rank Determination in Projective Nonnegative Matrix Factorization

Zhirong Yang, Zhanxing Zhu and Erkki Oja.
   In Proceedings of 9th International Conference on Latent Variable Analysis and Signal Separation. 2010.

29. A boundary corrected expansion of the moments of nearest neighbor distributions

Francesco Corona, Amaury Lendasse and Elia Liitiäinen.
   In Random Structures and Algorithms, volume 37, pages 223--247. September, 2010.

28. An Online Evaluation Platform for Proactive Information Retrieval Task

Li Yao and Antti Ajanki.
   In Proceedings of the 14th Finnish Artificial Intelligence Conference. 2010.

27. Ensembles of Locally Linear Models: Application to Bankruptcy Prediction

Laura Kainulainen, Qi Yu, Yoan Miche, Emil Eirola, Eric Séverin and Amaury Lendasse.
   In Proceedings of the 2010 International Conference on Data Mining, pages 280--286. July, 2010.

26. Evolving fuzzy optimally pruned extreme learning machine for regression problems

Federico Montesino Pouzols and Amaury Lendasse.
   In Evolving Systems, volume 1, pages 43--58. August, 2010.

25. Effect of Different Detrending Approaches on Computational Intelligence Models of Time Series

Federico Montesino Pouzols and Amaury Lendasse.
   In International Joint Conference on Neural Networks (IJCNN), pages 1729-1736. July, 2010.

24. Evolving Fuzzy Optimally Pruned Extreme Learning Machine: A Comparative Analysis

Federico Montesino Pouzols and Amaury Lendasse.
   In IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pages 1339--1346. July, 2010.

23. A continuous regression function for the Delaunay calibration method

Francesco Corona, Elia Liitiäinen, Amaury Lendasse, Roberto Baratti and Lorenzo Sassu.
   In Proceedings of IFAC/DYCOPS 2010 9th International Symposium on Dynamics and Control of Process Systems, Leuven (Belgium), pages 180--185. July 5-7, 2010.

22. On the topological modeling and analysis of industrial process data using the SOM

Francesco Corona, Michela Mulas, Roberto Baratti and Jose Romagnoli.
   In Computers and Chemical Engineering, volume 34, pages 2022--2032. 2010.

21. Identifying Fuzzy Inference Models by Means of Possibilistic Clustering: Socio-Economic Applications

Alberto Guillén, Federico Montesino Pouzols, Angel Barriga Barros, Luis Javier Herrera, Jesus Gonzalez, Hector Pomares and Ignacio Rojas.
   In Computational Methods for Modelling and Learning in Social and Human Sciences (MASHS). June, 2010.

20. Combination of SOMs for Fast Missing Value Imputation

Antti Sorjamaa, Amaury Lendasse and Eric Séverin.
   In Proceedings of MASHS 2010, Modèles et Apprentissage en Sciences Humaines et Sociale, Lille (France). June, 2010.

19. Multiple-Output Modelling for Multi-Step-Ahead Time Series Forecasting

Souhaib Ben Taieb, Antti Sorjamaa and Gianluca Bontempi.
   In Neurocomputing, volume 73, pages 1950-1957 . June, 2010.

18. Using Multiple Re-Embeddings For Quantitative Steganalysis and Image Reliability Estimation

Yoan Miche, Patrick Bas and Amaury Lendasse.
June, 2010.

17. Fast Missing Value Imputation Using Ensemble of SOMs

Antti Sorjamaa and Amaury Lendasse.
June, 2010.

16. Blind source separation in diffuse reflectance NIR spectroscopy using independent component analysis

Maunu Toiviainen, Francesco Corona, Janne Paaso and Pekka Teppola.
   In Journal of Chemometrics, volume 24, pages 514--522. May, 2010.

15. European Symposium on Times Series Prediction

Amaury Lendasse, Timo Honkela and Olli Simula.
   In Neurocomputing, volume 73, pages 1919--1922. June, 2010.

14. New method for instance or prototype selection using mutual information in time series prediction

Alberto Guillén, Luis Herrera, Gines Rubio, Amaury Lendasse and Hector Pomares.
   In Neurocomputing, volume 73, pages 2030--2038. June, 2010.

13. Extending Self-Organizing Maps with Uncertainty Information of Probabilistic PCA

Dušan Sovilj, Tapani Raiko and Erkki Oja.
   In IJCNN, pages 1915--1921. July 18-23, 2010.

12. Machine Learning Techniques Based on Random Projections

Yoan Miche, Benjamin Schrauwen and Amaury Lendasse.
   In ESANN2010: 18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pages 295--302. April 28--30, 2010.

11. Ensemble Modeling with a Constrained Linear System of Leave-One-Out Outputs

Yoan Miche, Emil Eirola, Patrick Bas, Olli Simula, Christian Jutten, Amaury Lendasse and Michel Verleysen.
   In ESANN2010: 18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pages 19--24. April 28--30, 2010.

10. Solving Large Regression Problems using an Ensemble of GPU-accelerated ELMs

Mark van Heeswijk, Yoan Miche, Erkki Oja and Amaury Lendasse.
   In ESANN2010: 18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pages 309--314. April 28--30, 2010.

9. Residual variance estimation using a nearest neighbor statistic

Elia Liitiäinen, Amaury Lendasse and Francesco Corona.
   In Journal of Multivariate Analysis, volume 101, pages 811--823 . April, 2010.

8. X-SOM and L-SOM: A Double Classification Approach for Missing Value Imputation

Paul Merlin, Antti Sorjamaa, Bertrand Maillet and Amaury Lendasse.
   In Neurocomputing, volume 73, pages 1103-1108. March, 2010.

7. OP-KNN: Method and Applications

Qi Yu, Yoan Miche, Antti Sorjamaa, Alberto Guillén, Amaury Lendasse and Eric Séverin.
   In Advances in Artificial Neural Systems, volume 2010, pages 6 pages. February, 2010.

6. An improved methodology for filling missing values in spatiotemporal climate data set

Antti Sorjamaa, Amaury Lendasse, Yves Cornet and Eric Deleersnijder.
   In Computational Geosciences, volume 14, pages 55-64. January, 2010.

5. Automatic Clustering-Based Identification of Autoregressive Fuzzy Inference Models for Time Series

Federico Montesino Pouzols and Angel Barriga Barros.
   In Neurocomputing, volume 73, pages 1937--1949 . August, 2010.

4. OP-ELM: Optimally-Pruned Extreme Learning Machine

Yoan Miche, Antti Sorjamaa, Patrick Bas, Olli Simula, Christian Jutten and Amaury Lendasse.
   In IEEE Transactions on Neural Networks, volume 21, pages 158--162. January, 2010.

3. Autoregressive Time Series Prediction by Means of Fuzzy Inference Systems Using Nonparametric Residual Variance Estimation

Federico Montesino Pouzols, Amaury Lendasse and Angel Barriga Barros.
   In Fuzzy Sets and Systems, volume 161, pages 471--497. February, 2010.

2. Approximate k-NN Delta Test Minimization Method using Genetic Algorithms: Application to Time Series

Fernando Mateo, Dušan Sovilj and Rafael Gadea.
   In Neurocomputing, volume 73, pages 2017-2029. June, 2010.

1. OPELM and OPKNN in long-term prediction of time series using projected input data

Dušan Sovilj, Antti Sorjamaa, Qi Yu, Yoan Miche and Eric Séverin.
   In Neurocomputing, volume 73, pages 1976-1986. June, 2010.

24. A Non-Linear Approach for Completing Missing Values in Temporal Databases

Antti Sorjamaa, Paul Merlin, Bertrand Maillet and Amaury Lendasse.
   In European Journal of Economic and Social Systems, volume 22, pages 99-117. November, 2009.

23. Variable Selection with the Delta Test in Theory and Practice

Emil Eirola.
November, 2009, Master's thesis.

22. RCGA-S/RCGA-SP Methods to Minimize the Delta Test for Regression Tasks

Fernando Mateo, Dušan Sovilj, Rafael Gadea and Amaury Lendasse.
   In IWANN 2009, volume 5517, pages 359-366. June 10-12, 2009.

21. Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction

Mark van Heeswijk, Yoan Miche, Tiina Lindh-Knuutila, Peter A.J. Hilbers, Timo Honkela, Erkki Oja and Amaury Lendasse.
   In ICANN 2009, Part II, volume 5769, pages 305-314. 2009.

20. Mutual Information Based Initialization of Forward-Backward Search for Feature Selection in Regression Problems

Alberto Guillén, Antti Sorjamaa, Gines Rubio, Amaury Lendasse and Ignacio Rojas.
   In LNCS - Artificial Neural Networks - ICANN 2009 – Part I, volume 5768, pages 1-9. September, 2009.

19. Fast Variable Selection using Delta Test

Dušan Sovilj.
September, 2009.

18. On the statistical estimation of Rényi entropies

Elia Liitiäinen, Amaury Lendasse and Francesco Corona.
   In Proceedings of IEEE/MLSP 2009 International Workshop on Machine Learning for Signal Processing, Grenoble (France). September 2-4, 2009.

17. A Fixed-Point Algorithm for Nonnegative Independent Component Analysis

Zhenwei Shi, Xueyan Tan, Zhanxing Zhu and Zhiguo Jiang.
   In Proceedings of 5th International Conference on Natural Computation, pages 482-485. 2009.

16. Quadratic Form Innovation to Blind Source Separation

Zhenwei Shi, Zhanxing Zhu, Xueyan Tan and Zhiguo Jiang.
   In Proceedings of 5th International Conference on Natural Computation, pages 594-597. 2009.

15. On the topological analysis of industrial process data using the SOM

Francesco Corona, Michela Mulas, Roberto Baratti and Jose Romagnoli.
   In Computer Aided Chemical Engineering: Proceedings of PSE 2009 International Symposium on Process Systems Engineering, Salvador Bahia (Brazil), volume 27, pages 1173--1178. August 16-20, 2009.

14. Delaunay tessellation and topological regression: An application to estimating product properties

Francesco Corona, Elia Liitiäinen, Amaury Lendasse, Roberto Baratti and Lorenzo Sassu.
   In Computer Aided Chemical Engineering: Proceedings of PSE 2009 International Symposium on Process Systems Engineering, Salvador Bahia (Brazil), volume 27, pages 1179--1184. August 16-20, 2009.

13. A SOM-based approach to estimating product properties from spectroscopic measurements

Francesco Corona, Elia Liitiäinen, Amaury Lendasse, Lorenzo Sassu, Stefano Melis and Roberto Baratti.
   In Neurocomputing, volume 73, pages 71--79. December, 2009.

12. Residual variance estimation in machine learning

Elia Liitiäinen, Michel Verleysen, Francesco Corona and Amaury Lendasse.
   In Neurocomputing, volume 72, pages 3692--3703. October, 2009.

11. Data derived analysis and inference for an industrial deethanizer

Francesco Corona, Michela Mulas, Roberto Baratti and Jose Romagnoli.
   In Proceedings of IFAC/ADCHEM 2009 International Symposium on Advanced Control of Chemical Processes, Istanbul (Turkey), pages 717--723. July 12-15, 2009.

10. Ensemble KNNs for Bankruptcy Prediction

Qi Yu, Amaury Lendasse and Eric Séverin.
   In CEF 09, 15th International Conference: Computing in Economics and Finance, Sydney. June 15-17, 2009.

9. Long-Term Prediction of Time Series by combining Direct and MIMO Strategies

Souhaib Ben Taieb, Gianluca Bontempi, Antti Sorjamaa and Amaury Lendasse.
   In International Joint Conference on Neural Networks. June, 2009.

8. Efficient Parallel Feature Selection for Steganography Problems

Alberto Guillén, Antti Sorjamaa, Yoan Miche, Amaury Lendasse and Ignacio Rojas.
   In LNCS - Bio-Inspired Systems: Computational and Ambient Intelligence – IWANN 2009, Part I, volume 5517/2009, pages 1224 – 1231. June, 2009.

7. Sparse linear combination of SOMs for data imputation: Application to financial database

Antti Sorjamaa, Francesco Corona, Yoan Miche, Paul Merlin, Bertrand Maillet, Eric Séverin and Amaury Lendasse.
   In Lecture Notes in Computer Science: Advances in Self-Organizing Maps - Proceedings of WSOM 2009 International Workshop on Self-Organizing Maps, Saint Augustine (Florida), volume 5629/2009, pages 290--297. June 8-10, 2009.

6. Linear combination of SOMs for data imputation: Application to financial problems

Antti Sorjamaa, Francesco Corona, Yoan Miche, Paul Merlin, Bertrand Maillet and Amaury Lendasse.
   In Proceedings of MASHS 2009, Modèles et Apprentissage en Sciences Humaines et Sociale, Lille (France). June 8-9, 2009.

5. Data analysis and inference for an industrial deethanizer

Francesco Corona, Michela Mulas, Roberto Baratti and Jose Romagnoli.
   In Chemical Engineering Transactions: Proceedings of ICHEAP9 International Conference on Chemical and Process Engineering, Rome (Italy), volume 17, pages 1197--1202. May 10-13, 2009.

4. A Faster Model Selection Criterion for OP-ELM and OP-KNN: Hannan-Quinn Criterion

Yoan Miche and Amaury Lendasse.
   In ESANN'09: European Symposium on Artificial Neural Networks, pages 177--182. April 22-24, 2009.

3. X-SOM and L-SOM: a Nested Approach for Missing Value Imputation

Paul Merlin, Antti Sorjamaa, Bertrand Maillet and Amaury Lendasse.
   In ESANN2009 proceedings, European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, pages 83-88. April, 2009.

2. Reliable Steganalysis Using a Minimum Set of Samples and Features

Yoan Miche, Patrick Bas, Amaury Lendasse, Christian Jutten and Olli Simula.
   In EURASIP Journal on Information Security, volume 2009, pages 1--13 (Article ID 901381). March, 2009, http://www.hindawi.com/journals/is/2009/901381.html.

1. A Feature Selection Methodology for Steganalysis

Yoan Miche, Patrick Bas, Amaury Lendasse, Christian Jutten and Olli Simula.
   In Traitement du Signal, volume 26, pages 13--30. May, 2009, http://apps.isiknowledge.com/full_record.do?product=WOS&search_mode=GeneralSearch&qid=1&SID=Q2CCC8GdiNg2eaCEBEH&page=1&doc=2.

24. Gaussian basis functions for chemometrics

Tuomas Kärnä, Francesco Corona and Amaury Lendasse.
   In Journal of Chemometrics, volume 22, pages 701--707. November-December, 2008.

23. On non-parametric residual variance estimation

Elia Liitiäinen, Francesco Corona and Amaury Lendasse.
   In Neural Processing Letters, volume 28, pages 155--167. December, 2008.

22. New Methodologies Based on Delta Test for Variable Selection in Regression Problems

Alberto Guillén, Dušan Sovilj, Fernando Mateo, Ignacio Rojas and Amaury Lendasse.
   In Workshop on Parallel Architectures and Bioinspired Algorithms. October 25-29, 2008.

21. A boundary corrected expansion of the moments of nearest neighbor distributions

Elia Liitiäinen, Francesco Corona and Amaury Lendasse.
October 18, 2008.

20. Wavelength selection using the measure of topological relevance on the Self-Organizing Map

Francesco Corona, Satu-Pia Reinikainen, Kari Aaljoki, Annikki Perkkiö, Elia Liitiäinen, Roberto Baratti, Amaury Lendasse and Olli Simula.
   In Journal of Chemometrics, volume 22, pages 610--620. November-December, 2008.

19. Tabu Search with Delta Test for Time Series Prediction using OP-KNN

Dušan Sovilj, Antti Sorjamaa and Yoan Miche.
   In ESTSP, European Symposium on Time Series Prediction, pages 187-196. September 17-19, 2008.

18. A variable selection approach based on the Delta Test for Extreme Learning Machine models

Fernando Mateo and Amaury Lendasse.
   In Proceedings of the European Symposium on Time Series Prediction, pages 57--66. September, 2008.

17. A methodology for time series prediction in Finance

Qi Yu, Antti Sorjamaa, Yoan Miche and Eric Séverin.
   In ESTSP, European Symposium on Time Series Prediction, pages 285-293. September 17-19, 2008.

16. Instance or Prototype Selection for Function Approximation using Mutual Information

Alberto Guillén, Luis Herrera, Gines Rubio, Amaury Lendasse, Hector Pomares and Ignacio Rojas.
   In ESTSP'08 Proceedings, pages 67-75. September, 2008.

15. ESTSP 2008: Proceedings

Amaury Lendasse.
   In European Symposium on Time Series Prediction, ESTSP'08. 2008, ISBN: 978-951-22-9544-9.

14. Regressive Fuzzy Inference Models with Clustering Identification: Application to the ESTSP08 Competition

Federico Montesino Pouzols and Angel Barriga Barros.
   In 2nd European Symposium on Time Series Prediction (ESTSP08), pages 205-214. September, 2008.

13. Optimal Pruned K-Nearest Neighbors: OP-KNN - Application to Financial Modeling

Qi Yu, Antti Sorjamaa, Yoan Miche, Amaury Lendasse, Alberto Guillén, Eric Séverin and Fernando Mateo.
   In Hybrid Intelligent Systems, 2008. Eighth International Conference on, pages 764-769. September, 2008.

12. Bounds on the mean power-weighted nearest neighbour distance

Elia Liitiäinen, Amaury Lendasse and Francesco Corona.
   In Proceedings of the Royal Society A, volume 464, pages 2293--2301. September, 2008.

11. xftsp: a Tool for Time Series Prediction by Means of Fuzzy Inference Systems

Federico Montesino Pouzols, Amaury Lendasse and Angel Barriga Barros.
   In 4th IEEE International Conference on Intelligent Systems (IS08), volume 1, pages 2-2--2-7. September, 2008.

10. OP-ELM: Theory, Experiments and a Toolbox

Yoan Miche, Antti Sorjamaa and Amaury Lendasse.
   In LNCS - Artificial Neural Networks - ICANN 2008 - Part I, volume 5163/2008, pages 145-154. September, 2008.

9. OP-KNN for Financial regression problems

Qi Yu, Antti Sorjamaa, Yoan Miche, Eric Séverin and Amaury Lendasse.
   In Mashs 08, Computational Methods for Modelling and learning in Social and Human Sciences, Creteil (France). June 5-6, 2008.

8. Long-Term Prediction of Time Series using NNE-based Projection and OP-ELM

Antti Sorjamaa, Yoan Miche, Robert Weiss and Amaury Lendasse.
   In IEEE World Conference on Computational Intelligence, pages 2675-2681. June, 2008.

7. Fuzzy Inference Based Autoregressors for Time Series Prediction Using Nonparametric Residual Variance Estimation

Federico Montesino Pouzols, Amaury Lendasse and Angel Barriga Barros.
   In 17th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'2008), IEEE World Congress on Computational Intelligence, pages 613-618. June, 2008.

6. Nonlinear temporal and spatial forecasting: modelling and uncertainty analysis (NoTeS) – MASIT20

Risto Ritala, Esa Alhoniemi, Tuomo Kauranne, Kimmo Konkarikoski, Amaury Lendasse and Miki Sirola.
   In MASI Programme 2005–2009,Yearbook 2008, pages 163-175. 2008.

5. Developing chemometrics with the tools of information sciences (CHESS) -- MASIT23

Olli Simula, Francesco Corona, Amaury Lendasse, Marja-Liisa Riekkola, Kari Hartonen, Pentti Minkkinen, Satu-Pia Reinikainen, Jarno Kohonen, Ilppo Vuorinen, Jari Hänninen and Jukka Silén.
   In MASI Programme 2005-2009, Yearbook 2008, pages 189--222. May, 2008.

4. Using the Delta test for variable selection

Emil Eirola, Elia Liitiäinen, Amaury Lendasse, Francesco Corona and Michel Verleysen.
   In Proceedings of ESANN 2008, European Symposium on Artificial Neural Networks, Bruges (Belgium), pages 25--30. April 23-25, 2008.

3. A methodology for Building Regression Models using Extreme Learning Machine: OP-ELM

Yoan Miche, Patrick Bas, Christian Jutten, Olli Simula and Amaury Lendasse.
   In ESANN 2008, European Symposium on Artificial Neural Networks, Bruges, Belgium, pages 247--252. April 23-25, 2008.

2. Linear projection based on noise variance estimation: Application to spectral data

Amaury Lendasse and Francesco Corona.
   In Proceedings of ESANN 2008, European Symposium on Artificial Neural Networks, Bruges (Belgium), pages 457--462. April 23-25, 2008.

1. Minimizing the Delta Test for Variable Selection in Regression Problems

Alberto Guillén, Dušan Sovilj, Fernando Mateo, Ignacio Rojas and Amaury Lendasse.
   In International Journal of High Performance Systems Architecture, volume 1, pages 269-281. 2008.

29. Optimal linear projection based on noise variance estimation

Amaury Lendasse and Francesco Corona.
   In Proceedings of Chimiométrie 2007, Lyon (France), pages 165--168. November 29-30, 2007.

28. Functional variable selection using noise variance estimation

Amaury Lendasse, Francesco Corona, Satu-Pia Reinikainen and Pentti Minkkinen.
   In Proceedings of Chimiométrie 2007, Lyon (France), pages 39--42. November 29-30, 2007.

27. Compressing spectral data using optimised Gaussian basis

Tuomas Kärnä, Francesco Corona and Amaury Lendasse.
   In Proceedings of Chimiométrie 2007, Lyon (France), pages 177--180. November 29-30, 2007.

26. Avantages de la Sélection de Caractéristiques pour la Stéganalyse

Yoan Miche, Patrick Bas, Amaury Lendasse, Olli Simula and Christian Jutten.
   In GRETSI 2007, Groupe de Recherche et d'Etudes du Traitement du Signal et des Images, Troyes, France. September 11-13, 2007.

25. Measures of topological relevance based on the Self-Organizing Map: Applications to process monitoring from spectroscopic measurements

Francesco Corona, Elia Liitiäinen, Amaury Lendasse and Roberto Baratti.
   In Proceedings of EANN 2007, International Conference on Engineering Applications of Neural Networks, Thessaloniki (Greece), pages 24--33. August 29-31, 2007.

24. Variable Scaling for Time Series Prediction: Application to the ESTSP'07 and the NN3 Forecasting Competitions

Elia Liitiäinen and Amaury Lendasse.
   In IJCNN 2007, International Joint Conference on Neural Networks, Orlando, Florida, USA, pages 2812 - 2816. August, 2007.

23. Time Series Prediction as a Problem of Missing Values: Application to ESTSP2007 and NN3 Competition Benchmarks

Antti Sorjamaa, Elia Liitiäinen and Amaury Lendasse.
   In IJCNN, International Joint Conference on Neural Networks, pages 1770-1775. August 12-17, 2007.

22. Time Series Prediction Competition: The CATS Benchmark

Amaury Lendasse, Erkki Oja, Olli Simula and Michel Verleysen.
   In Neurocomputing, volume 70, pages 2325-2329. August, 2007.

21. Gaussian fitting based FDA for chemometrics

Tuomas Kärnä and Amaury Lendasse.
   In IWANN'07, International Work-Conference on Artificial Neural Networks, San Sebastian, Spain, volume 4507, pages 186--193. June, 2007.

20. Advantages of Using Feature Selection Techniques on Steganalysis Schemes

Yoan Miche, Patrick Bas, Amaury Lendasse, Christian Jutten and Olli Simula.
   In IWANN'07: International Work-Conference on Artificial Neural Networks, San Sebastian, Spain, volume 4507/2007, pages 606--613. June 20-22, 2007.

19. Non-parametric residual variance estimation in supervised learning

Elia Liitiäinen, Francesco Corona and Amaury Lendasse.
   In Lecture Notes in Computer Science: Computational and Ambient Intelligence - Proceedings of IWANN 2007 International Work-Conference on Artificial Neural Networks, San Sebastian (Spain), volume 4507/2007, pages 63--71. June 20-22, 2007.

18. Extracting Relevant Features of Steganographic Schemes by Feature Selection Techniques

Yoan Miche, Patrick Bas, Amaury Lendasse, Christian Jutten and Olli Simula.
   In Wacha'07: Third Wavilla Challenge. June 14, 2007.

17. Variable Selection for Financial Modeling

Qi Yu, Eric Séverin and Amaury Lendasse.
   In CEF 2007, 13th International Conference on Computing in Economics and Finance MontrĂ©al, Quebec, Canada. June 14 -16, 2007.

16. Optimal Gaussian Basis Functions for Chemometrics

Tuomas Kärnä and Amaury Lendasse.
   In SSC10, 10th Scandinavian Symposium on Chemometrics, Lappeenranta (Finland), pages 79. June 11-15, 2007.

15. Optimal linear projection based on noise variance estimation: Application to spectrometric modeling

Amaury Lendasse and Francesco Corona.
   In Proceedings of SSC10 Scandinavian Symposium on Chemometrics, Lappeenranta (Finland), pages 26. June 11-15, 2007.

14. Using functional representations in spectrophotoscopic variables selection and regression

Francesco Corona, Elia Liitiäinen and Amaury Lendasse.
   In Proceedings of SSC10 Scandinavian Symposium on Chemometrics, Lappeenranta (Finland), pages 29. June 11-15, 2007.

13. Measure of topological relevance for soft sensing product properties

Francesco Corona, Lorenzo Sassu, Stefano Melis and Roberto Baratti.
   In Proceedings of IFAC/DYCOPS 2007 International Symposium on Dynamics and Control of Process Systems, Cancun (Mexico), pages 175--180. June 6-8, 2007.

12. Methodology for Long-term Prediction of Time Series

Antti Sorjamaa, Jin Hao, Nima Reyhani, Yongnan Ji and Amaury Lendasse.
   In Neurocomputing, volume 70, pages 2861-2869. October, 2007.

11. A Global Methodology for Variable Selection: Application to Financial Modeling

Qi Yu, Eric Séverin and Amaury Lendasse.
   In Mashs 2007, Computational Methods for Modelling and learning in Social and Human Sciences, Brest (France). May 10-11, 2007.

10. A Nonlinear Approach for the Determination of Missing Values in Temporal Databases

Antti Sorjamaa, Paul Merlin, Bertrand Maillet and Amaury Lendasse.
   In MASHS, Computational Methods for Modelling and learning in Social and Human Sciences, Brest (France). May 10-11, 2007.

9. Nearest neighbor distributions and noise variance estimation

Elia Liitiäinen, Francesco Corona and Amaury Lendasse.
   In Proceedings of ESANN 2007, European Symposium on Artificial Neural Networks, Bruges (Belgium), pages 67-72. April 25-27, 2007.

8. SOM+EOF for Finding Missing Values

Antti Sorjamaa, Paul Merlin, Bertrand Maillet and Amaury Lendasse.
   In ESANN 2007, European Symposium on Artificial Neural Networks, Bruges (Belgium), pages 115-120. April 25-27, 2007.

7. State-of-the-art and Evolution in Public Data Sets and Competitions for System Identification, Time Series Prediction and Pattern Recognition

Joos Vandewalle, Johan Suykens, Bart De Moor and Amaury Lendasse.
   In 32nd International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Hawaii Convention Center in Honolulu (USA), volume 4, pages 1269--1272. April 15-20, 2007.

6. Developing chemometrics with the tools of information sciences (CHESS) – MASIT23

Olli Simula, Amaury Lendasse, Francesco Corona, Satu-Pia Reinikainen, Marja-Liisa Riekkola, Kari Hartonen, Ilppo Vuorinen and Jukka Silén.
   In MASI Programme 2005-2009, Yearbook 2007, pages 201--221. March, 2007.

5. An Empirical Dependence Measures Based on Residual Variance Estimation

Nima Reyhani and Amaury Lendasse.
   In ISSPA 2007, International Symposium on Signal Processing and its Applications in conjunction with the International Conference on Information Sciences, Signal Processing and its Applications, Sharjah, United Arab Emirates (U.A.E.), pages 1-4. February 12-15, 2007.

4. Time Series Prediction as a Problem of Missing Values

Antti Sorjamaa and Amaury Lendasse.
   In ESTSP 2007, European Symposium on Time Series Prediction, Espoo (Finland), pages 165-174. February 7-9, 2007.

3. Variable scaling for time series prediction

Francesco Corona and Amaury Lendasse.
   In Proceedings of ESTSP 2007, European Symposium on Time Series Prediction, Espoo (Finland), pages 69--76. February 7-9, 2007.

2. Comparison of FDA based Time Series Prediction Methods

Tuomas Kärnä and Amaury Lendasse.
   In ESTSP 2007, European Symposium on Time Series Prediction, Espoo (Finland), pages 77--86. February 7-9, 2007.

1. ESTSP 2007: Proceedings

Amaury Lendasse.
   In European Symposium on Time Series Prediction, ESTSP'07. 2007, ISBN: 978-951-22-8601-0.

6. A Feature Selection Methodology for Steganalysis

Yoan Miche, Benoit Roue, Patrick Bas and Amaury Lendasse.
   In MRCS06, International Workshop on Multimedia Content Representation, Classification and Security, Istanbul (Turkey), volume 4105, pages 49-56. September 11-13, 2006.

5. Analysis of Fast Input Selection: Application in Time Series Prediction

Jarkko Tikka, Amaury Lendasse and Jaakko Hollmén.
   In ICANN06, International Conference on Artificial Neural Networks, 16th International Conference, Athens (Greece), volume 4132/2006, pages 161--170. September 10-14, 2006.

4. Long-Term Prediction of Time Series Using State-Space Models

Elia Liitiäinen and Amaury Lendasse.
   In ICANN'06, International Conference on Artificial Neural Networks, 16th International Conference, Athens (Greece), volume 4132/2006, pages 181--190. September 10-14, 2006.

3. Time Series Prediction using DirRec Strategy

Antti Sorjamaa and Amaury Lendasse.
   In ESANN06, European Symposium on Artificial Neural Networks, pages 143-148. April 26-28, 2006.

2. Determination of the Mahalanobis matrix using non-parametric noise estimations

Amaury Lendasse, Francesco Corona, Jin Hao, Nima Reyhani and Michel Verleysen.
   In Proceedings of ESANN 2006, European Symposium on Artificial Neural Networks, Bruges (Lille), pages 227--232. April 26-28, 2006.

1. Mutual information for the selection of relevant variables in spectrometric nonlinear modelling

Fabrice Rossi, Amaury Lendasse, Damien François, Vincent Wertz and Michel Verleysen.
   In Chemometrics and Intelligent Laboratory Systems, volume 80, pages 215--226. February, 2006.

18. Nonparametric Noise Estimation to Build Nonlinear Model in Chemometry

Amaury Lendasse, Damien François, Vincent Wertz and Michel Verleysen.
   In Chimiométrie 2005, Villeneuve d'Ascq (France), pages 143--146. November 30 - December 1, 2005.

17. Least Squares Support Vector Machines for Time Series Prediction

Yongnan Ji.
October 14, 2005, Master's Thesis obtained with the grade "passed with distinction" ("kiitäen hyväksytty"), 5/5.

16. Strategies for the Long-Term Prediction of Time Series using Local Models

Antti Sorjamaa.
October 14, 2005, Master Thesis obtained with the grade 5.

15. LS-SVM Hyperparameter Selection with a Nonparametric Noise Estimator

Amaury Lendasse, Yongnan Ji, Nima Reyhani and Michel Verleysen.
   In ICANN05, International Conference on Artificial Neural Networks, Artificial Neural Networks: Formal Models and Their Applications, volume 3697, pages 625--630. September 11-15, 2005.

14. Input selection and function approximation using the Self-Organizing Map: An application to spectrometric modeling

Francesco Corona and Amaury Lendasse.
   In Proceedings of WSOM 2005 International Workshop on Self-Organizing Maps, Paris (France), pages 653--660. September 5-8, 2005.

13. Input Selection using Mutual Information: Applications to Time Series Prediction

Jin Hao.
September 2, 2005, Master's Thesis obtained with the grade "passed with distinction" ("kiitäen hyväkstty"), 5/5.

12. Time Series Forecasting: Obtaining Long Term Trends with Self-Organizing Maps

Geoffroy Simon, Amaury Lendasse, Marie Cottrell, Jean-Claude Fort and Michel Verleysen.
   In Pattern Recognition Letters, volume 26, pages 1795--1808. September, 2005.

11. Analysis and monitoring of air emissions using neural techniques

Francesco Corona, Alberto Servida and Stefania Tronci.
   In Proceedings of EANN 2005 International Conference on Engineering Applications of Neural Networks, Lille (France), pages 101--108. August 24-26, 2005.

10. Comparing neural networks and regression models for air quality management

Stefania Tronci, Francesco Corona, Massimiliano Grosso, R. Calento and Francesca Murena.
   In Proceedings of EANN 2005 International Conference on Engineering Applications of Neural Networks, Lille (France), pages 93--100. August 24-26, 2005.

9. Mutual Information and k-Nearest Neighbors approximator for Time Series Predictions

Antti Sorjamaa, Jin Hao and Amaury Lendasse.
   In LNCS - Artificial Neural Networks: Formal Models and Their Applications - ICANN 2005, volume 3697/2005, pages 553-558. September 11-15, 2005.

8. Direct and Recursive Prediction of Time Series Using Mutual Information Selection

Yongnan Ji, Jin Hao, Nima Reyhani and Amaury Lendasse.
   In Computational Intelligence and Bioinspired Systems: 8th International Workshop on Artificial Neural Networks, IWANN'05, Vilanova i la Geltra, Barcelona, Spain, volume 3512, pages 1010--1017. June 8-10, 2005.

7. Input and Structure Selection for k-NN Approximator

Antti Sorjamaa, Nima Reyhani and Amaury Lendasse.
   In LNCS - Computational Intelligence and Bioinspired Systems - IWANN 2005, volume 3512/2005, pages 985--992. June, 2005.

6. Input Selection for Long-Term Prediction of Time Series

Jarkko Tikka, Jaakko Hollmén and Amaury Lendasse.
   In Computational Intelligence and Bioinspired Systems: 8th International Workshop on Artificial Neural Networks, IWANN 2005, Vilanova i la Geltra, Barcelona, Spain, volume 3512, pages 1002--1009. June, 2005.

5. Application of neural soft sensing techniques to emission monitoring from multiple combustion processes

Francesco Corona, Stefania Tronci, Paolo Bragatto, Paolo Pittiglio, Alberto Servida, Claudio Codevico and Roberto Baratti.
   In Chemical Engineering Transactions: Proceedings of ICHEAP7 International Conference on Chemical and Process Engineering, Giardini di Naxos (Italy), volume 6, pages 167--172. May 15-18, 2005.

4. Mutual Information and Gamma Test for Input Selection

Nima Reyhani, Jin Hao, Yongnan Ji and Amaury Lendasse.
   In ESANN 2005, European Symposium on Artificial Neural Networks, Bruges (Belgium), pages 503--508. April 27-29, 2005.

3. Pruned Lazy Learning Models for Time Series Prediction

Antti Sorjamaa, Amaury Lendasse and Michel Verleysen.
   In ESANN05, European Symposium on Artificial Neural Networks, pages 509-514. April 27-29, 2005.

2. Fast bootstrap methodology for regression model selection

Amaury Lendasse, Geoffroy Simon, Vincent Wertz and Michel Verleysen.
   In Neurocomputing, volume 64, pages 161--181. March, 2005.

1. Application of neural soft sensing techniques to emission monitoring from multiple combustion processes

Francesco Corona, Stefania Tronci, Paolo Bragatto, Paolo Pittiglio, Alberto Servida, Claudio Codevico and Roberto Baratti.
   In AIDIC Conference Series, volume 7, pages 59--66. 2005.

10. Sélection de variables spectrales par information mutuelle multivariée pour la construction de modčles non-linéaires

Amaury Lendasse, Damien François, Fabrice Rossi, Vincent Wertz and Michel Verleysen.
   In Chimiométrie 2004, Paris (France), pages 44--47. November 30 - December 1, 2004.

9. Business Plans Classification with Locally Pruned Lazy Learning Models

Antti Sorjamaa, Amaury Lendasse, Damien François and Michel Verleysen.
   In ACSEG 2004, Connectionist Approaches in Economics and Management Sciences, Lille (France), pages 112-119. November 18-19, 2004.

8. Robust Time Series Prediction Using KIII Model

Igor Beliaev, Robert Kozma and Amaury Lendasse.
   In IDS04 Symposium, FedEx Institute of Technology (FIT), University of Memphis, TN, USA, pages April 24-26. Published, 2004.

7. Double Quantization of the Regressor Space for Long-Term Time Series Prediction: Method and Proof of Stability

Geoffroy Simon, Amaury Lendasse, Marie Cottrell, Jean-Claude Fort and Michel Verleysen.
   In Neural Networks, volume 17, pages 1169--1181. October-November, 2004, Special Issue.

6. Fast Bootstrap applied to LS-SVM for Long Term Prediction of Time Series

Amaury Lendasse, Vincent Wertz, Geoffroy Simon and Michel Verleysen.
   In Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on, volume 1, pages 705--710. July, 2004.

5. Time Series Prediction Competition: The CATS Benchmark

Amaury Lendasse, Erkki Oja, Olli Simula and Michel Verleysen.
   In IJCNN 2004, International Joint Conference on Neural Networks, volume 2, pages 1615--1620. July, 25-29, 2004.

4. Potentials of soft sensors for continuous emission monitoring systems

Fabio Callero, Paolo Moretti, Christian Parisi, Alberto Servida, Francesco Corona, Paolo Bragatto and Claudio Codevico.
   In Proceedings of CEM 2004 International Conference on Emission Monitoring, Milan (Italy). June 9-11, 2004.

3. Fast Bootstrap for Least-square Support Vector Machines

Amaury Lendasse, Geoffroy Simon, Robert Kozma, Vincent Wertz and Michel Verleysen.
   In ESANN 2004, European Symposium on Artificial Neural Networks, Bruges (Belgium), pages 525--530. April 28-30, 2004.

2. Nonlinear projection with curvilinear distances: Isomap versus curvilinear distance analysis

John A. Lee, Amaury Lendasse and Michel Verleysen.
   In Neurocomputing, volume 57, pages 49--76. March, 2004.

1. Self-organizing feature maps for the classification of investment funds

Eric de Bodt, Amaury Lendasse, Pierre Cardon and Michel Verleysen.
   In Journal of Economic and Social Systems, volume 17, pages 183--195. 2004.

14. Financial Time Series Forecasting by Double SOM Maps and Local RBF Models Forecasting the DAX30 Index

Simon Dablemont, Geoffroy Simon, Amaury Lendasse, Alain Ruttiens and Michel Verleysen.
   In ACSEG 2003, Rencontre Internationale sur les Approches Connexionnistes en Sciences Economiques et de Gestion, Nantes (France), pages 153--164. November 20-21, 2003.

13. Are business plans usefull for investors ?

Damien François, Amaury Lendasse, Benoit Gailly, Vincent Wertz and Michel Verleysen.
   In ACSEG 2003, Connectionist Approaches in Economics and Management Sciences, Nantes (France), pages 239--249. November 20-21, 2003.

12. Le test des méthodes neuronales – ou comment utiliser les techniques de rééchantillonnage pour ne pas se tromper de résultat

Michel Verleysen and Amaury Lendasse.
   In ACSEG 2003 proceedings - Connectionist Approaches in Economics and Management Sciences, Nantes (France), pages 515--534. November 20-21, 2003.

11. Analyse et prédiction de séries temporelles par méthodes non linéaires: Application à des données industrielles et financières

Amaury Lendasse.
2003.

10. Long-Term Time Series Forecasting using Self-Organizing Maps: the Double Vector Quantization Method

Geoffroy Simon, Amaury Lendasse, Marie Cottrell and Michel Verleysen.
   In ANNPR 2003, Artificial Neural Networks in Pattern Recognition, Florence (Italy), pages 8--14. September 12-13, 2003.

9. Time series forecasting with SOM and local non-linear models - Application to the DAX30 index prediction

Simon Dablemont, Geoffroy Simon, Amaury Lendasse, Alain Ruttiens, François Blayo and Michel Verleysen.
   In Proceedings of the Workshop on Self-organizing Maps, pages 340--345. September 11-14, 2003.

8. Double SOM for Long-term Time Series Prediction

Geoffroy Simon, Amaury Lendasse, Marie Cottrell, Jean-Claude Fort and Michel Verleysen.
   In WSOM 2003, Workshop on Self-Organizing Maps, pages 35--40. September 11-14, 2003.

7. Approximation by Radial-Basis Function networks - Application to option pricing

Amaury Lendasse, John A. Lee, Eric de Bodt, Vincent Wertz and Michel Verleysen.
Advances in Computational Management Science, C. Lesage and M. Cottrell editors, Chapter 10 in Connectionist Approaches in Economics and Management Sciences, volume 6, pages 203--214 2003.

6. Fast Approximation of the Bootstrap for Model Selection

Geoffroy Simon, Amaury Lendasse, Vincent Wertz and Michel Verleysen.
   In ESANN 2003, European Symposium on Artificial Neural Networks, Bruges (Belgium), pages 99--106. April 23-25, 2003.

5. Model Selection with Cross-Validations and Bootstraps - Application to Time Series Prediction with RBFN Models

Amaury Lendasse, Vincent Wertz and Michel Verleysen.
   In ICANN 2003, Joint International Conference on Artificial Neural Networks, Istanbul (Turkey), volume 2714, pages 573--580. June 26-29, 2003.

4. Nonlinear Time Series Prediction by Weighted Vector Quantization

Amaury Lendasse, Damien François, Vincent Wertz and Michel Verleysen.
   In Computational Science — ICCS 2003, volume 2657--1, pages 417--426. January, 2003.

3. Bootstrap for Model Selection: Linear Approximation of the Optimism

Geoffroy Simon, Amaury Lendasse and Michel Verleysen.
   In IWANN 2003, International Work-Conference on Artificial and Natural Neural Networks, Mao, Menorca (Spain), volume 2686--1, pages 182--189. June 3-6, 2003.

2. Should Seed Investors Read Business Plans?

Damien François, Benoit Gailly, Amaury Lendasse, Vincent Wertz and Michel Verleysen.
   In 22th Benelux Meeting on Systems and Control, Lommel, Belgium. March 19-21, 2003.

1. Fast Bootstrap for Model Structure Selection

Amaury Lendasse, Geoffroy Simon, Vincent Wertz and Michel Verleysen.
   In 22th Benelux Meeting on Systems and Control, Lommel, Belgium, pages 81. March 19-21, 2003.

5. Curvilinear Distance Analysis versus Isomap

Amaury Lendasse and Michel Verleysen.
   In ESANN 2002, European Symposium on Artificial Neural Networks, Bruges (Belgium), pages 185--192. April, 2002.

4. Classification of investment funds by self-organizing maps

Pierre Cardon, Amaury Lendasse, Vincent Wertz, Eric de Bodt and Michel Verleysen.
   In ACSEG 2002, Connectionist Approaches in Economics and Management Sciences, Boulogne-sur-Mer (France), pages 201--212. November 21-22, 2002.

3. Forecasting electricity consumption using nonlinear projection and self-organizing maps

Amaury Lendasse, John A. Lee, Vincent Wertz and Michel Verleysen.
   In Neurocomputing, volume 48, pages 299--311. October, 2002.

2. Prediction of Electric Load using Kohonen Maps - Application to the Polish Electricity Consumption

Amaury Lendasse, Marie Cottrell, Vincent Wertz and Michel Verleysen.
   In ACC 2002, American Control Conference, Anchorage, Alaska (USA), pages 3684--3689. June, 2002.

1. Width optimization of the Gaussian kernels in Radial Basis Function Networks

Nabil Benoudjit, Cédric Archambeau, Amaury Lendasse, John A. Lee and Michel Verleysen.
   In ESANN 2002, European Symposium on Artificial Neural Networks, Bruges (Belgium), pages 425--432. April, 2002.

7. Phosphene evaluation in a visual prosthesis with artificial neural networks

Cédric Archambeau, Amaury Lendasse, Charles Trullemans, Claude Veraart, Jean Delbeke and Michel Verleysen.
   In Adaptive Systems and Hybrid Computational Intelligence in Medicine, special session proceedings of EUNITE 2001, Tenerife (Spain), pages 116--122. December 13-14, 2001.

6. Phosphene evaluation in a visual prosthesis with artificial neural networks

Cédric Archambeau, Amaury Lendasse, Charles Trullemans, Claude Veraart, Jean Delbeke and Michel Verleysen.
   In EUNITE 2001, European Symposium on Intelligent Technologies, Hybrid Systems and their implementation on Smart Adaptive Systems, Tenerife (Spain), pages 509--515. December 13-14, 2001.

5. Approximation using Radial Basis Functions Networks - Application to Pricing Derivative Securities

Amaury Lendasse, John A. Lee, Eric de Bodt, Vincent Wertz and Michel Verleysen.
   In ACSEG 2001, Connectionist Approaches in Economics and Management Sciences, Rennes (France), pages 275--283. November 22-23, 2001.

4. Input data reduction for the prediction of financial time series

Amaury Lendasse, John A. Lee, Eric de Bodt, Vincent Wertz and Michel Verleysen.
   In ESANN 2001, European Symposium on Artificial Neural Networks, Bruges (Belgique), pages 237--244. April, 2001.

3. Forecasting electricity demand using Kohonen maps

Amaury Lendasse, Vincent Wertz and Michel Verleysen.
   In 20th Benelux meeting on Systems and Control, Houffalize (Belgium), pages 118. March, 2001.

2. Nonlinear financial time series forecasting - Application to the Bel 20 stock market index

Amaury Lendasse, Eric de Bodt, Vincent Wertz and Michel Verleysen.
   In European Journal of Economic and Social Systems, volume 14, pages 81--92. February, 2001.

1. Dimension reduction of technical indicators for the prediction of financial time series, Application to the Bel 20 market index

Amaury Lendasse, John A. Lee, Vincent Wertz, Eric de Bodt and Michel Verleysen.
   In European Journal of Economic and Social Systems, volume 15, pages 31--48. 2001.

4. Réduction de la dimension d'un ensemble d'indicateurs techniques en vue de la prédiction de séries temporelles financières - Application à l'indice de marché BEL 20

Amaury Lendasse, John A. Lee, Eric de Bodt, Vincent Wertz and Michel Verleysen.
   In ACSEG 2000, 7emes rencontres internationales. December, 2000.

3. A robust non-linear projection method

John A. Lee, Amaury Lendasse, N. Donckers and Michel Verleysen.
   In ESANN'2000, European Symposium on Artificial Neural Networks, Bruges (Belgique), pages 13--20. April, 2000.

2. Time series forecasting using CCA and Kohonen maps - application to electricity consumption

Amaury Lendasse, John A. Lee, Vincent Wertz and Michel Verleysen.
   In ESANN'2000, European Symposium on Artificial Neural Networks, Bruges (Belgique), pages 329--334. April, 2000.

1. Statistical fault isolation with PCA

G. Gomez and Amaury Lendasse.
   In IFAC, Safeprocess'. 2000.

3. Forecasting financial time series through intrinsic dimension estimation and non-linear data projection

Michel Verleysen, Eric de Bodt and Amaury Lendasse.
   In IWANN99, International Work-conference on Artificial and Natural Neural networks, Alicante (Spain). Published in Engineering Applications of Bio-Inspired Artificial Neural Networks, volume 1607--2, pages 596--605. June, 1999.

2. Extraction of intrinsic dimension using CCA - Application to blind sources separation

N. Donckers, Amaury Lendasse, Vincent Wertz and Michel Verleysen.
   In ESANN'99, European Symposium on Artificial Neural Networks, Bruges (Belgique), pages 339--344. April, 1999.

1. Comparison Between NAR and NARMA Models for Time-Series Prediction: Choice of a Non-Linear Regressor Vector

Amaury Lendasse.
   In 18th Benelux Meeting on Systems and Control, Conference Center "Hengelhoef", Houthalen, Belgium. March 3-5, 1999.

3. Estimation de la dimension intrinsèque d'une série temporelle et prédiction par une méthode de projection

Amaury Lendasse, Eric de Bodt and Michel Verleysen.
   In ACSEG'98, Association Connectioniste en Sciences Economiques et de Gestion, Louvain-la-Neuve (Belgique), pages D37-D46. November 20, 1998.

2. Identification of fuzzy models for a glass furnace process

M. L. Hadjili, Amaury Lendasse, Vincent Wertz and S. Yurkovich.
   In 1998 IEEE International Conference on Control Applications,Trieste, Italy, pages 963-968. September 1-4, 1998.

1. Forecasting time-series by Kohonen classification

Amaury Lendasse, Michel Verleysen, Eric de Bodt, Marie Cottrell and P. Gregoire.
   In ESANN'98, European Symposium on Artificial Neural Networks, Bruges (Belgique), pages 221--226. April, 1998.