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Amaury Lendasse

Chief Research Scientist, Docent

Office:
Room T-B308 in Computer Science Building,
Konemiehentie 2, Otaniemi campus area, Espoo
Postal Address:
Aalto University, School of Science and Technology,
Department of Information and Computer Science,
P.O. Box 15400, FI-00076 Aalto, Finland
Telephone:
+358 40 770 0237
Email:
lendasse atttt hut putadothere fi

Description

I am born in Tournai, Belgium on April 16th, 1972. I got my Master Degree in Mechanical Engineering in the Universite de Louvain-la-Neuve in Belgium in 1997 and a second Master Degree in Control in the same university in 1997. I got my PhD degree in 2003 in Louvain-la-Neuve under the supervision of Prof. Vincent Wertz and Michel Verleysen. I am now a Docent and senior researcher in ICS, HUT.

Research

Machine Learning, Time-Series Prediction, Environmental Modeling, Industrial Applications, Information Security, Variable Selection, GPU, ... Too many things indeed, but luckily I work 50 hours a week :)

Abstract

I am the instructor of Yu Qi, Yoan Miche, Elia Liitiäinen, Mark van Heeswijk, Emil Eirola, Dusan Sovilj and Antti Sorjamaa. I hope that soon some of them will complete their PhD.

Publications

List of Publications. Order by:   Type | Date


2014

2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 2000 | 1999 | 1998


10. Variable Selection for Regression Problems Using Gaussian Mixture Models to Estimate Mutual Informationdoipdf filebibtex file

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 Criteriondoipdf filebibtex file

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 Lyrpdf filebibtex file

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-ELMbibtex file

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 Testurldoipdf filebibtex file

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 datadoipdf filebibtex file

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 Researchdoipdf filebibtex file

Rui Nian, Bo He, Bing Zheng, Mark van Heeswijk, Qi Yu, Yoan Miche and Amaury Lendasse.
   In Neurocomputing. 2014, Available online 6 November 2013.

3. Ensemble Delta Test- Extreme Learning Machine (DT-ELM) For Regressiondoipdf filebibtex file

Qi Yu, Mark van Heeswijk, Yoan Miche, Rui Nian, Bo He, Eric Séverin and Amaury Lendasse.
   In Neurocomputing. 2014, Available online 29 October 2013.

2. Fast Face Recognition Via Sparse Coding and Extreme Learning Machinedoipdf filebibtex file

Bo He, Dongxun Xu, Rui Nian, Mark van Heeswijk, Qi Yu, Yoan Miche and Amaury Lendasse.
   In Cognitive Computation. 2014, Available online 13 July 2013.

1. Long-term Time Series Prediction using OP-ELMurldoipdf filebibtex file

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

2013

2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 2000 | 1999 | 1998


11. ELMVIS: a Nonlinear Visualization Technique using Random Permutations and Extreme Learning Machineurldoipdf filebibtex file

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

10. Gaussian Mixture Models for Time Series Modelling, Forecasting, and Interpolationdoipdf filebibtex file

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.

9. Practical Estimation of Missing Phosphorus Values in Pyhajarvi Lake Dataurlpdf filebibtex file

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.

8. Image-based Classification of Websitesurlpdf filebibtex file

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.

7. Distance Estimation in Numerical Data Sets with Missing Valuesdoipdf filebibtex file

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

6. Extending Extreme Learning Machine with Combination Layerpdf filebibtex file

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 Agentsurldoipdf filebibtex file

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 Classificationbibtex file

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 Expertisebibtex file

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

2. Feature Selection for Nonlinear Models using Extreme Learning Machinesdoipdf filebibtex file

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 Datadoipdf filebibtex file

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

2012

2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 2000 | 1999 | 1998


4. Mixture of Gaussians for Distance Estimation with Missing Dataurlbibtex file

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.

3. Evolutive Approaches for Variable Selection Using a Non-parametric Noise Estimatordoibibtex file

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.

2. Fast variable selection for memetracker phrases time series predictionurldoibibtex file

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.

1. Relevance learning for time series inspectionurlpdf filebibtex file

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

2011

2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 2000 | 1999 | 1998


12. Methodology for Behavioral-based Malware Analysis and Detection using Random Projections and K-Nearest Neighbors Classifierspdf filebibtex file

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

11. Ensembles of Local Linear Models for Bankruptcy Analysis and Predictionurlpdf filebibtex file

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.

10. Random Projection Method for Scalable Malware Classificationurlpdf filebibtex file

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

9. Local linear regression for soft-sensor design with application to an industrial deethanizerurlpdf filebibtex file

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.

8. Local Linear Regression for Soft-Sensor Design with Application to an Industrial Deethanizerdoibibtex file

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.

7. Bankruptcy Prediction with Missing Datapdf filebibtex file

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

6. Adaptive Kernel Smoothing Regression for Spatio-Temporal Environmental Datasetspdf filebibtex file

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.

5. Locating Anomalies Using Bayesian Factorizations and Maskspdf filebibtex file

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.

4. On the Curse of Dimensionality in Supervised Learning of Smooth Regression Functionsdoibibtex file

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

3. GPU-Accelerated and Parallelized ELM Ensembles for Large-scale Regressiondoipdf filebibtex file

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 Regularizationdoipdf filebibtex file

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)urldoipdf filebibtex file

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

2010

2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 2000 | 1999 | 1998


21. Interpreting Extreme Learning Machine as an Approximation to an Infinite Neural Network pdf filebibtex file

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.

20. A boundary corrected expansion of the moments of nearest neighbor distributionsdoipdf filebibtex file

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

19. Ensembles of Locally Linear Models: Application to Bankruptcy Predictionpdf filebibtex file

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.

18. Evolving fuzzy optimally pruned extreme learning machine for regression problemsurldoipdf filebibtex file

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

17. Effect of Different Detrending Approaches on Computational Intelligence Models of Time Seriespdf filebibtex file

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

16. Evolving Fuzzy Optimally Pruned Extreme Learning Machine: A Comparative Analysisurldoipdf filebibtex file

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

15. A continuous regression function for the Delaunay calibration methodbibtex file

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.

14. Combination of SOMs for Fast Missing Value Imputationpdf filebibtex file

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

13. Using Multiple Re-Embeddings For Quantitative Steganalysis and Image Reliability Estimationurlpdf filebibtex file

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

12. Fast Missing Value Imputation Using Ensemble of SOMsurlbibtex file

Antti Sorjamaa and Amaury Lendasse.
June, 2010.

11. European Symposium on Times Series Predictiondoipdf filebibtex file

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

10. New method for instance or prototype selection using mutual information in time series predictiondoipdf filebibtex file

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

9. Machine Learning Techniques Based on Random Projectionspdf filebibtex file

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.

8. Ensemble Modeling with a Constrained Linear System of Leave-One-Out Outputspdf filebibtex file

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.

7. Solving Large Regression Problems using an Ensemble of GPU-accelerated ELMspdf filebibtex file

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.

6. Residual variance estimation using a nearest neighbor statistic urldoibibtex file

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

5. X-SOM and L-SOM: A Double Classification Approach for Missing Value Imputationurldoipdf filebibtex file

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

4. OP-KNN: Method and Applicationsurldoipdf filebibtex file

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.

3. An improved methodology for filling missing values in spatiotemporal climate data set urldoipdf filebibtex file

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

2. OP-ELM: Optimally-Pruned Extreme Learning Machinedoipdf filebibtex file

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.

1. Autoregressive Time Series Prediction by Means of Fuzzy Inference Systems Using Nonparametric Residual Variance Estimationurldoipdf filebibtex file

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

2009

2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 2000 | 1999 | 1998


17. A Non-Linear Approach for Completing Missing Values in Temporal Databasesdoipdf filebibtex file

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

16. RCGA-S/RCGA-SP Methods to Minimize the Delta Test for Regression Tasksdoipdf filebibtex file

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

15. Adaptive Ensemble Models of Extreme Learning Machines for Time Series Predictiondoipdf filebibtex file

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.

14. Mutual Information Based Initialization of Forward-Backward Search for Feature Selection in Regression Problemsurldoipdf filebibtex file

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.

13. On the statistical estimation of Rényi entropiesdoipdf filebibtex file

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.

12. Delaunay tessellation and topological regression: An application to estimating product properties urldoibibtex file

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.

11. A SOM-based approach to estimating product properties from spectroscopic measurementsdoipdf filebibtex file

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

10. Residual variance estimation in machine learning doipdf filebibtex file

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

9. Ensemble KNNs for Bankruptcy Predictionpdf filebibtex file

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

8. Long-Term Prediction of Time Series by combining Direct and MIMO Strategiespdf filebibtex file

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

7. Efficient Parallel Feature Selection for Steganography Problemsurldoipdf filebibtex file

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.

6. Sparse linear combination of SOMs for data imputation: Application to financial databaseurldoipdf filebibtex file

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.

5. Linear combination of SOMs for data imputation: Application to financial problemspdf filebibtex file

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.

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

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 Imputationurlpdf filebibtex file

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 Featuresdoipdf filebibtex file

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 Steganalysispdf filebibtex file

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.

2008

2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 2000 | 1999 | 1998


21. Gaussian basis functions for chemometricsurldoipdf filebibtex file

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

20. On non-parametric residual variance estimationurldoipdf filebibtex file

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

19. New Methodologies Based on Delta Test for Variable Selection in Regression Problemspdf filebibtex file

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

18. A boundary corrected expansion of the moments of nearest neighbor distributionsurlpdf filebibtex file

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

17. Wavelength selection using the measure of topological relevance on the Self-Organizing Mapurldoipdf filebibtex file

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.

16. A variable selection approach based on the Delta Test for Extreme Learning Machine modelspdf filebibtex file

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

15. Instance or Prototype Selection for Function Approximation using Mutual Informationpdf filebibtex file

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

14. ESTSP 2008: Proceedingspdf filebibtex file

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

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

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 distancedoipdf filebibtex file

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 Systemsurldoipdf filebibtex file

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 Toolboxdoipdf filebibtex file

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 problemspdf filebibtex file

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-ELMdoipdf filebibtex file

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 Estimationurldoipdf filebibtex file

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) – MASIT20pdf filebibtex file

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) -- MASIT23bibtex file

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 pdf filebibtex file

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-ELMpdf filebibtex file

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 datapdf filebibtex file

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 Problemsdoipdf filebibtex file

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.

2007

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28. Optimal linear projection based on noise variance estimationbibtex file

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

27. Functional variable selection using noise variance estimationbibtex file

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

26. Compressing spectral data using optimised Gaussian basisbibtex file

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

25. Avantages de la Sélection de Caractéristiques pour la Stéganalysepdf filebibtex file

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.

24. Measures of topological relevance based on the Self-Organizing Map: Applications to process monitoring from spectroscopic measurementsbibtex file

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.

23. Variable Scaling for Time Series Prediction: Application to the ESTSP'07 and the NN3 Forecasting Competitionsdoipdf filebibtex file

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

22. Time Series Prediction as a Problem of Missing Values: Application to ESTSP2007 and NN3 Competition Benchmarksdoipdf filebibtex file

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

21. Time Series Prediction Competition: The CATS Benchmarkurldoipdf filebibtex file

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

20. Gaussian fitting based FDA for chemometricsdoipdf filebibtex file

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.

19. Advantages of Using Feature Selection Techniques on Steganalysis Schemesdoipdf filebibtex file

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.

18. Non-parametric residual variance estimation in supervised learningdoipdf filebibtex file

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.

17. Extracting Relevant Features of Steganographic Schemes by Feature Selection Techniquespdf filebibtex file

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

16. Variable Selection for Financial Modelingpdf filebibtex file

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.

15. Optimal Gaussian Basis Functions for Chemometricspdf filebibtex file

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

14. Optimal linear projection based on noise variance estimation: Application to spectrometric modelingpdf filebibtex file

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

13. Using functional representations in spectrophotoscopic variables selection and regressionpdf filebibtex file

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

12. Methodology for Long-term Prediction of Time Seriesurldoipdf filebibtex file

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 Modelingpdf filebibtex file

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 Databasespdf filebibtex file

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 estimationpdf filebibtex file

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 Valuespdf filebibtex file

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 Recognitiondoipdf filebibtex file

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) – MASIT23bibtex file

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 Estimationdoipdf filebibtex file

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 Valuespdf filebibtex file

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 predictionpdf filebibtex file

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 Methodspdf filebibtex file

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: Proceedingspdf filebibtex file

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

2006

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6. A Feature Selection Methodology for Steganalysisdoipdf filebibtex file

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 Predictiondoipdf filebibtex file

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 Modelsdoipdf filebibtex file

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 Strategypdf filebibtex file

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 estimationsbibtex file

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 modellingurldoipdf filebibtex file

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

2005

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11. Nonparametric Noise Estimation to Build Nonlinear Model in Chemometrypdf filebibtex file

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

10. LS-SVM Hyperparameter Selection with a Nonparametric Noise Estimatordoipdf filebibtex file

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.

9. Input selection and function approximation using the Self-Organizing Map: An application to spectrometric modelingpdf filebibtex file

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.

8. Time Series Forecasting: Obtaining Long Term Trends with Self-Organizing Mapsurldoipdf filebibtex file

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

7. Mutual Information and k-Nearest Neighbors approximator for Time Series Predictionsurldoipdf filebibtex file

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.

6. Direct and Recursive Prediction of Time Series Using Mutual Information Selectiondoipdf filebibtex file

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.

5. Input and Structure Selection for k-NN Approximatordoipdf filebibtex file

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

4. Input Selection for Long-Term Prediction of Time Seriespdf filebibtex file

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.

3. Mutual Information and Gamma Test for Input Selectionpdf filebibtex file

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.

2. Pruned Lazy Learning Models for Time Series Predictionpdf filebibtex file

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

1. Fast bootstrap methodology for regression model selectionurldoipdf filebibtex file

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

2004

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9. Sélection de variables spectrales par information mutuelle multivariée pour la construction de modčles non-linéairespdf filebibtex file

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

8. Business Plans Classification with Locally Pruned Lazy Learning Modelspdf filebibtex file

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.

7. Robust Time Series Prediction Using KIII Modelbibtex file

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.

6. Double Quantization of the Regressor Space for Long-Term Time Series Prediction: Method and Proof of Stabilityurldoipdf filebibtex file

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.

5. Fast Bootstrap applied to LS-SVM for Long Term Prediction of Time Seriespdf filebibtex file

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.

4. Time Series Prediction Competition: The CATS Benchmarkpdf filebibtex file

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.

3. Fast Bootstrap for Least-square Support Vector Machinespdf filebibtex file

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 analysisurldoipdf filebibtex file

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 fundsbibtex file

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

2003

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

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 ?pdf filebibtex file

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 résultatpdf filebibtex file

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 financièrespdf filebibtex file

Amaury Lendasse.
2003.

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

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 predictionpdf filebibtex file

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 Predictionpdf filebibtex file

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 pricingurlpdf filebibtex file

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 Selectionpdf filebibtex file

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 Modelsurldoipdf filebibtex file

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 Quantizationurldoipdf filebibtex file

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 Optimismdoipdf filebibtex file

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?bibtex file

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 Selectionpdf filebibtex file

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.

2002

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5. Curvilinear Distance Analysis versus Isomappdf filebibtex file

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 mapspdf filebibtex file

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 mapsurlpdf filebibtex file

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 Consumptionpdf filebibtex file

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 Networkspdf filebibtex file

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.

2001

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7. Phosphene evaluation in a visual prosthesis with artificial neural networkspdf filebibtex file

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 networksbibtex file

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 Securitiespdf filebibtex file

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 seriespdf filebibtex file

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 mapsbibtex file

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 indexurlpdf filebibtex file

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 indexpdf filebibtex file

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.

2000

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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 20pdf filebibtex file

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 methodpdf filebibtex file

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 consumptionpdf filebibtex file

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 PCAbibtex file

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

1999

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3. Forecasting financial time series through intrinsic dimension estimation and non-linear data projectiondoipdf filebibtex file

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 separationpdf filebibtex file

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 Vectorbibtex file

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

1998

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

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 processdoipdf filebibtex file

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 classificationpdf filebibtex file

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.



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