to appear |
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162 | Benoît Frénay, Mark van Heeswijk, Yoan Miche, Michel Verleysen, and Amaury Lendasse. Feature selection for nonlinear models using extreme learning machines. Neurocomputing, to appear. accepted for presentation at International Symposium on Extreme Learning Machines. |
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161 | Qi Yu, Emil Eirola, Mark van Heeswijk, Eric Séverin, and Amaury Lendasse. Regularized extreme learning machine for regression with missing data. Neurocomputing, to appear. accepted for presentation at International Symposium on Extreme Learning Machines. |
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2011 |
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160 | József Hegedüs, Yoan Miche, Alexander Ilin, and Amaury Lendasse. Methodology for behavioral-based malware analysis and detection using random projections and k-nearest neighbors classifiers. In 7th International Conference on Computational Intelligence and Security (CIS2011), Sanya, China, December 2011. |
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159 | Laura Kainulainen, Yoan Miche, Emil Eirola, Qi Yu, Benoît Frénay, Eric Séverin, and Amaury Lendasse. Ensembles of local linear models for bankruptcy analysis and prediction. Case Studies in Business, Industry and Government Statistics (CSBIGS), 4(2), November 2011. |
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158 | Mark van Heeswijk, Yoan Miche, Erkki Oja, and Amaury Lendasse. GPU-accelerated and parallelized ELM ensembles for large-scale regression. Neurocomputing, 74(16):2430–2437, September 2011. |
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157 | Yoan Miche, Mark van Heeswijk, Patrick Bas, Olli Simula, and Amaury Lendasse. TROP-ELM: a double-regularized ELM using LARS and tikhonov regularization. Neurocomputing, 74(16):2413–2421, September 2011. |
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156 | József Hegedüs, Yoan Miche, Alexander Ilin, and Amaury Lendasse. Random projection method for scalable malware classification. In 14th International Symposium on Recent Advances in Intrusion Detection, California, USA, September 2011. |
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155 | Zhanxing Zhu, Francesco Corona, Amaury Lendasse, Roberto Baratti, and Jose Romagnoli. Local linear regression for soft-sensor design with application to an industrial deethanizer. In 18th World Congress of the International Federation of Automatic Control (IFAC), Milano, Italy, August 2011. |
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154 | Anne-Mari Ventelä, Teija Kirkkala, Amaury Lendasse, Marjo Tarvainen, Harri Helminen, and Jouko Sarvala. Climate-related challenges in long-term management of säkylän pyhäjärvi (SW finland). Hydrobiologia, 660:49–58, 2011. |
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153 | Elia Liitiäinen, Francesco Corona, and Amaury Lendasse. On the curse of dimensionality in supervised learning of smooth regression functions. Neural Processing Letters, 34(2):133–154, 2011. |
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152 | Federico Montesino Pouzols and Amaury Lendasse. Adaptive kernel smoothing regression for spatio-temporal environmental datasets. In ESANN 2011 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pages 87–92, Bruges, Belgium, 2011. |
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151 | Li Yao, Amaury Lendasse, and Francesco Corona. Locating anomalies using bayesian factorizations and masks. In ESANN 2011 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pages 207–212, Bruges, Belgium, 2011. |
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150 | Zhanxing Zhu, Francesco Corona, Amaury Lendasse, Roberto Baratti, and Jose Romagnoli. Local linear regression for soft-sensor design with application to an industrial deethanizer. In Proceedings of the 18th IFAC World Congress, volume 18, pages 2839–2844, Università Cattolica del Sacro Cuore, Milano, Italy, 2011. |
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2010 |
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149 | Elina Parviainen, Jaakko Riihimäki, Yoan Miche, and Amaury Lendasse. Interpreting extreme learning machine as an approximation to an infinite neural network. In KDIR 2010: Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, Valencia, Spain, October 2010. |
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148 | Francesco Corona, Amaury Lendasse, and Elia Liitiäinen. A boundary corrected expansion of the moments of nearest neighbor distributions. Random Structures and Algorithms, 37(2):223–247, September 2010. |
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147 | Federico Montesino Pouzols and Amaury Lendasse. Evolving fuzzy optimally pruned extreme learning machine for regression problems. Evolving Systems, 1(1):43–58, August 2010. |
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146 | Francesco Corona, Elia Liitiäinen, Amaury Lendasse, Roberto Baratti, and Lorenzo Sassu. A continuous regression function for the delaunay calibration method. 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. |
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145 | Federico Montesino Pouzols and Amaury Lendasse. Effect of different detrending approaches on computational intelligence models of time series. In International Joint Conference on Neural Networks (IJCNN), pages 1729–1736, Barcelona, Spain, July 2010. |
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144 | Federico Montesino Pouzols and Amaury Lendasse. Evolving fuzzy optimally pruned extreme learning machine: A comparative analysis. In IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pages 1339–1346, Barcelona, Spain, July 2010. |
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143 | Laura Kainulainen, Qi Yu, Yoan Miche, Emil Eirola, Eric Séverin, and Amaury Lendasse. Ensembles of locally linear models: Application to bankruptcy prediction. In Robert Stahlbock and Sven F. Crone, editors, Proceedings of the 2010 International Conference on Data Mining, pages 280–286. Worldcomp10, July 2010. |
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142 | Amaury Lendasse, Timo Honkela, and Olli Simula. European symposium on times series prediction. Neurocomputing, 73(10–12):1919–1922, June 2010. |
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141 | Alberto Guillén, Luis Herrera, Gines Rubio, Amaury Lendasse, and Hector Pomares. New method for instance or prototype selection using mutual information in time series prediction. Neurocomputing, 73(10–12):2030–2038, June 2010. |
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140 | Yoan Miche, Patrick Bas, and Amaury Lendasse. Using multiple re-embeddings for quantitative steganalysis and image reliability estimation. Technical Report TKK-ICS-R34, Aalto University School of Science and Technology, Aalto, Finland, June 2010. |
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139 | Antti Sorjamaa, Amaury Lendasse, and Eric Séverin. Combination of SOMs for fast missing value imputation. In Proceedings of MASHS 2010, Modèles et Apprentissage en Sciences Humaines et Sociale, Lille (France). Models and learnings in Human and social Sciences, June 2010. |
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138 | Antti Sorjamaa and Amaury Lendasse. Fast missing value imputation using ensemble of SOMs. Technical Report TKK-ICS-R33, Aalto University School of Science and Technology, June 2010. |
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137 | Yoan Miche, Benjamin Schrauwen, and Amaury Lendasse. Machine learning techniques based on random projections. In Michel Verleysen, editor, ESANN2010: 18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pages 295–302, Bruges, Belgium, April 28–30 2010. d-side Publications. |
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136 | Yoan Miche, Emil Eirola, Patrick Bas, Olli Simula, Christian Jutten, Amaury Lendasse, and Michel Verleysen. Ensemble modeling with a constrained linear system of leave-one-out outputs. In Michel Verleysen, editor, ESANN2010: 18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pages 19–24, Bruges, Belgium, April 28–30 2010. d-side Publications. |
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135 | Mark van Heeswijk, Yoan Miche, Erkki Oja, and Amaury Lendasse. Solving large regression problems using an ensemble of GPU-accelerated ELMs. In Michel Verleysen, editor, ESANN2010: 18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pages 309–314, Bruges, Belgium, April 28–30 2010. d-side Publications. |
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134 | Elia Liitiäinen, Amaury Lendasse, and Francesco Corona. Residual variance estimation using a nearest neighbor statistic. Journal of Multivariate Analysis, 101(4):811–823, April 2010. |
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133 | Paul Merlin, Antti Sorjamaa, Bertrand Maillet, and Amaury Lendasse. X-SOM and l-SOM: A double classification approach for missing value imputation. Neurocomputing, 73(7-9):1103–1108, March 2010. |
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132 | Federico Montesino Pouzols, Amaury Lendasse, and Angel Barriga Barros. Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation. Fuzzy Sets and Systems, 161(4):471–497, February 2010. |
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131 | Qi Yu, Yoan Miche, Antti Sorjamaa, Alberto Guillén, Amaury Lendasse, and Eric Séverin. OP-KNN: Method and applications. Advances in Artificial Neural Systems, 2010(597373):6 pages, February 2010. |
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130 | Antti Sorjamaa, Amaury Lendasse, Yves Cornet, and Eric Deleersnijder. An improved methodology for filling missing values in spatiotemporal climate data set. Computational Geosciences, 14:55–64, January 2010. |
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129 | Yoan Miche, Antti Sorjamaa, Patrick Bas, Olli Simula, Christian Jutten, and Amaury Lendasse. OP-ELM: Optimally-pruned extreme learning machine. IEEE Transactions on Neural Networks, 21(1):158–162, January 2010. |
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2009 |
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128 | Francesco Corona, Elia Liitiäinen, Amaury Lendasse, Lorenzo Sassu, Stefano Melis, and Roberto Baratti. A SOM-based approach to estimating product properties from spectroscopic measurements. Neurocomputing, 73(1–3):71–79, December 2009. |
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127 | Antti Sorjamaa, Paul Merlin, Bertrand Maillet, and Amaury Lendasse. A non-linear approach for completing missing values in temporal databases. European Journal of Economic and Social Systems, 22(1):99–117, November 2009. |
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126 | Elia Liitiäinen, Michel Verleysen, Francesco Corona, and Amaury Lendasse. Residual variance estimation in machine learning. Neurocomputing, 72(16–18):3692–3703, October 2009. |
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125 | Elia Liitiäinen, Amaury Lendasse, and Francesco Corona. On the statistical estimation of rényi entropies. In Proceedings of IEEE/MLSP 2009 International Workshop on Machine Learning for Signal Processing, Grenoble (France), September 2-4 2009. |
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124 | Alberto Guillén, Antti Sorjamaa, Gines Rubio, Amaury Lendasse, and Ignacio Rojas. Mutual information based initialization of forward-backward search for feature selection in regression problems. In C. Alippi, M.M. Polycarpou, C. Panayiotou, and G. Ellinas, editors, LNCS - Artificial Neural Networks - ICANN 2009 – Part I, volume 5768 of Lecture Notes in Computer Science, pages 1–9. ICANN, Springer Berlin / Heidelberg, September 2009. |
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123 | Francesco Corona, Elia Liitiäinen, Amaury Lendasse, Roberto Baratti, and Lorenzo Sassu. Delaunay tessellation and topological regression: An application to estimating product properties. In Evaristo Biscaia Rita de Brito Alves, Claudio Oller do Nascimento, editor, Computer Aided Chemical Engineering: Proceedings of PSE 2009 International Symposium on Process Systems Engineering, Salvador Bahia (Brazil), volume 27 of Computer Aided Chemical Engineering, pages 1179–1184. Elsevier, August 16-20 2009. |
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122 | Antti Sorjamaa, Francesco Corona, Yoan Miche, Paul Merlin, Bertrand Maillet, Eric Séverin, and Amaury Lendasse. Sparse linear combination of SOMs for data imputation: Application to financial database. In Risto Principe, J.C.; Miikkulainen, editor, 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 of Lecture Notes in Computer Science, pages 290–297. Springer Berlin / Heidelberg, June 8-10 2009. |
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121 | Alberto Guillén, Antti Sorjamaa, Yoan Miche, Amaury Lendasse, and Ignacio Rojas. Efficient parallel feature selection for steganography problems. In J. Cabestany, F. Sandoval A. Prieto, and J.M. Corchado, editors, LNCS - Bio-Inspired Systems: Computational and Ambient Intelligence – IWANN 2009, Part I, volume 5517/2009 of Lecture Notes in Computer Science, page 1224 – 1231. IWANN, Springer Berlin / Heidelberg, June 2009. |
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120 | Souhaib Ben Taieb, Gianluca Bontempi, Antti Sorjamaa, and Amaury Lendasse. Long-term prediction of time series by combining direct and MIMO strategies. In International Joint Conference on Neural Networks, Atlanta, Georgia, USA, June 2009. |
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119 | Antti Sorjamaa, Francesco Corona, Yoan Miche, Paul Merlin, Bertrand Maillet, and Amaury Lendasse. Linear combination of SOMs for data imputation: Application to financial problems. In Proceedings of MASHS 2009, Modèles et Apprentissage en Sciences Humaines et Sociale, Lille (France). MASHS, June 8-9 2009. |
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118 | Fernando Mateo, DuÅ¡an Sovilj, Rafael Gadea, and Amaury Lendasse. RCGA-s/RCGA-SP methods to minimize the delta test for regression tasks. In IWANN 2009, volume 5517 of Lecture Notes in Computer Science, pages 359–366, Salamanca, Spain, June 10-12 2009. Springer. |
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117 | Qi Yu, Amaury Lendasse, and Eric Séverin. Ensemble KNNs for bankruptcy prediction. In CEF 09, 15th International Conference: Computing in Economics and Finance, Sydney, June 15-17 2009. |
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116 | Yoan Miche, Patrick Bas, Amaury Lendasse, Christian Jutten, and Olli Simula. A feature selection methodology for steganalysis. Traitement du Signal, 26(1):13–30, May 2009. http://apps.isiknowledge.com/full_record.do?product=WOS&search_mode=GeneralSearch&qid=1&SID=Q2CCC8GdiNg2eaCEBEH&page=1&doc=2. |
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115 | Yoan Miche and Amaury Lendasse. A faster model selection criterion for OP-ELM and OP-KNN: Hannan-quinn criterion. In Michel Verleysen, editor, ESANN'09: European Symposium on Artificial Neural Networks, pages 177–182. d-side publications, April 22-24 2009. |
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114 | Paul Merlin, Antti Sorjamaa, Bertrand Maillet, and Amaury Lendasse. X-SOM and l-SOM: a nested approach for missing value imputation. In Michel Verleysen, editor, ESANN2009 proceedings, European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, ESANN Proceedings, pages 83–88, Brugge, Belgium, April 2009. ESANN, d-side publications. |
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113 | Yoan Miche, Patrick Bas, Amaury Lendasse, Christian Jutten, and Olli Simula. Reliable steganalysis using a minimum set of samples and features. EURASIP Journal on Information Security, 2009(1):1–13 (Article ID 901381), March 2009. http://www.hindawi.com/journals/is/2009/901381.html. |
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112 | Mark van Heeswijk, Yoan Miche, Tiina Lindh-Knuutila, Peter A.J. Hilbers, Timo Honkela, Erkki Oja, and Amaury Lendasse. Adaptive ensemble models of extreme learning machines for time series prediction. In Cesare Alippi, Marios M. Polycarpou, Christos G. Panayiotou, and Georgios Ellinas, editors, ICANN 2009, Part II, volume 5769 of LNCS, pages 305–314, Heidelberg, 2009. Springer. |
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2008 |
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111 | Elia Liitiäinen, Francesco Corona, and Amaury Lendasse. On non-parametric residual variance estimation. Neural Processing Letters, 28(3):155–167, December 2008. |
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110 | Francesco Corona, Satu-Pia Reinikainen, Kari Aaljoki, Annikki Perkkiö, Elia Liitiäinen, Roberto Baratti, Amaury Lendasse, and Olli Simula. Wavelength selection using the measure of topological relevance on the self-organizing map. Journal of Chemometrics, 22(11–12):610–620, November-December 2008. |
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109 | Tuomas Kärnä, Francesco Corona, and Amaury Lendasse. Gaussian basis functions for chemometrics. Journal of Chemometrics, 22(11–12):701–707, November-December 2008. |
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108 | Alberto Guillén, DuÅ¡an Sovilj, Fernando Mateo, Ignacio Rojas, and Amaury Lendasse. New methodologies based on delta test for variable selection in regression problems. In Workshop on Parallel Architectures and Bioinspired Algorithms, Toronto, Canada, October 25-29 2008. |
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107 | Elia Liitiäinen, Francesco Corona, and Amaury Lendasse. A boundary corrected expansion of the moments of nearest neighbor distributions. Technical Report TKK-ICS-R9, Helsinki University of Technology, October 18 2008. |
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106 | Elia Liitiäinen, Amaury Lendasse, and Francesco Corona. Bounds on the mean power-weighted nearest neighbour distance. Proceedings of the Royal Society A, 464(2097):2293–2301, September 2008. |
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105 | Federico Montesino Pouzols, Amaury Lendasse, and Angel Barriga Barros. xftsp: a tool for time series prediction by means of fuzzy inference systems. In 4th IEEE International Conference on Intelligent Systems (IS08), volume 1, pages 2–2–2–7, Varna, Bulgaria, September 2008. |
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104 | Qi Yu, Antti Sorjamaa, Yoan Miche, Amaury Lendasse, Alberto Guillén, Eric Séverin, and Fernando Mateo. Optimal pruned k-nearest neighbors: OP-KNN - application to financial modeling. In Fatos Xhafa, Francisco Herrera, Ajith Abraham, Mario Köppen, and Jose Manuel Bénitez, editors, Hybrid Intelligent Systems, 2008. Eighth International Conference on, pages 764–769, Barcelona, Spain, September 2008. |
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103 | Yoan Miche, Antti Sorjamaa, and Amaury Lendasse. OP-ELM: Theory, experiments and a toolbox. In Roman Neruda Vera Kurková and Jan Koutník, editors, LNCS - Artificial Neural Networks - ICANN 2008 - Part I, volume 5163/2008 of Lecture Notes in Computer Science, pages 145–154. Springer Berlin / Heidelberg, September 2008. |
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102 | Fernando Mateo and Amaury Lendasse. A variable selection approach based on the delta test for extreme learning machine models. In M. Verleysen, editor, Proceedings of the European Symposium on Time Series Prediction, pages 57–66. d-side publ. (Evere, Belgium), September 2008. |
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101 | Alberto Guillén, Luis Herrera, Gines Rubio, Amaury Lendasse, Hector Pomares, and Ignacio Rojas. Instance or prototype selection for function approximation using mutual information. In Amaury Lendasse, editor, ESTSP'08 Proceedings, pages 67–75, September 2008. |
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100 | Federico Montesino Pouzols, Amaury Lendasse, and Angel Barriga Barros. Fuzzy inference based autoregressors for time series prediction using nonparametric residual variance estimation. In 17th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'2008), IEEE World Congress on Computational Intelligence, pages 613–618, Hong Kong, China, June 2008. |
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99 | Antti Sorjamaa, Yoan Miche, Robert Weiss, and Amaury Lendasse. Long-term prediction of time series using NNE-based projection and OP-ELM. In IEEE World Conference on Computational Intelligence, pages 2675–2681, Hong Kong, June 2008. Research Publishing Services, Chennai, India. |
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98 | Qi Yu, Antti Sorjamaa, Yoan Miche, Eric Séverin, and Amaury Lendasse. OP-KNN for financial regression problems. In Mashs 08, Computational Methods for Modelling and learning in Social and Human Sciences, Creteil (France), June 5-6 2008. |
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97 | 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. Developing chemometrics with the tools of information sciences (CHESS) – MASIT23. In MASI Programme 2005-2009, Yearbook 2008, pages 189–222. Libris Oy, May 2008. |
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96 | Yoan Miche, Patrick Bas, Christian Jutten, Olli Simula, and Amaury Lendasse. A methodology for building regression models using extreme learning machine: OP-ELM. In M. Verleysen, editor, ESANN 2008, European Symposium on Artificial Neural Networks, Bruges, Belgium, pages 247–252. d-side publ. (Evere, Belgium), April 23-25 2008. |
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95 | Emil Eirola, Elia Liitiäinen, Amaury Lendasse, Francesco Corona, and Michel Verleysen. Using the delta test for variable selection. In M. Verleysen, editor, Proceedings of ESANN 2008, European Symposium on Artificial Neural Networks, Bruges (Belgium), pages 25–30. d-side publ. (Evere, Belgium), April 23-25 2008. |
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94 | Amaury Lendasse and Francesco Corona. Linear projection based on noise variance estimation: Application to spectral data. In M. Verleysen, editor, Proceedings of ESANN 2008, European Symposium on Artificial Neural Networks, Bruges (Belgium), pages 457–462. d-side publ. (Evere, Belgium), April 23-25 2008. |
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93 | Alberto Guillén, DuÅ¡an Sovilj, Fernando Mateo, Ignacio Rojas, and Amaury Lendasse. Minimizing the delta test for variable selection in regression problems. International Journal of High Performance Systems Architecture, 1(4):269–281, 2008. |
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92 | Risto Ritala, Esa Alhoniemi, Tuomo Kauranne, Kimmo Konkarikoski, Amaury Lendasse, and Miki Sirola. Nonlinear temporal and spatial forecasting: modelling and uncertainty analysis (notes) – MASIT20. In MASI Programme 2005–2009,Yearbook 2008, pages 163–175, 2008. |
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91 | Amaury Lendasse, editor. ESTSP 2008: Proceedings. Multiprint Oy / Otamedia, 2008. ISBN: 978-951-22-9544-9. |
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2007 |
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90 | Amaury Lendasse and Francesco Corona. Optimal linear projection based on noise variance estimation. In Proceedings of Chimiométrie 2007, Lyon (France), pages 165–168, November 29-30 2007. |
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89 | Amaury Lendasse, Francesco Corona, Satu-Pia Reinikainen, and Pentti Minkkinen. Functional variable selection using noise variance estimation. In Proceedings of Chimiométrie 2007, Lyon (France), pages 39–42, November 29-30 2007. |
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88 | Tuomas Kärnä, Francesco Corona, and Amaury Lendasse. Compressing spectral data using optimised gaussian basis. In Proceedings of Chimiométrie 2007, Lyon (France), pages 177–180, November 29-30 2007. |
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87 | Antti Sorjamaa, Jin Hao, Nima Reyhani, Yongnan Ji, and Amaury Lendasse. Methodology for long-term prediction of time series. Neurocomputing, 70(16-18):2861–2869, October 2007. |
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86 | Yoan Miche, Patrick Bas, Amaury Lendasse, Olli Simula, and Christian Jutten. Avantages de la sélection de caractéristiques pour la stéganalyse. In GRETSI 2007, Groupe de Recherche et d'Etudes du Traitement du Signal et des Images, Troyes, France, Troyes, France, September 11-13 2007. |
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85 | Amaury Lendasse, Erkki Oja, Olli Simula, and Michel Verleysen. Time series prediction competition: The CATS benchmark. Neurocomputing, 70(13-15):2325–2329, August 2007. |
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84 | Francesco Corona, Elia Liitiäinen, Amaury Lendasse, and Roberto Baratti. Measures of topological relevance based on the self-organizing map: Applications to process monitoring from spectroscopic measurements. In Proceedings of EANN 2007, International Conference on Engineering Applications of Neural Networks, Thessaloniki (Greece), pages 24–33, August 29-31 2007. |
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83 | Antti Sorjamaa, Elia Liitiäinen, and Amaury Lendasse. Time series prediction as a problem of missing values: Application to ESTSP2007 and NN3 competition benchmarks. In IJCNN, International Joint Conference on Neural Networks, pages 1770–1775, Orlando, Florida, USA, August 12-17 2007. Documation LLC, Eau Claire, Wisconsin, USA. |
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82 | Elia Liitiäinen and Amaury Lendasse. Variable scaling for time series prediction: Application to the ESTSP'07 and the NN3 forecasting competitions. In IJCNN 2007, International Joint Conference on Neural Networks, Orlando, Florida, USA, pages 2812 – 2816. Documation LLC, Eau Claire, Wisconsin, USA, August 2007. |
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81 | Yoan Miche, Patrick Bas, Amaury Lendasse, Christian Jutten, and Olli Simula. Advantages of using feature selection techniques on steganalysis schemes. In Francisco Sandoval et al., editor, IWANN'07: International Work-Conference on Artificial Neural Networks, San Sebastian, Spain, volume 4507/2007 of Lecture Notes in Computer Science, pages 606–613. Springer Berlin / Heidelberg, June 20-22 2007. |
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80 | Tuomas Kärnä and Amaury Lendasse. Gaussian fitting based FDA for chemometrics. In Francisco Sandoval et al., editor, IWANN'07, International Work-Conference on Artificial Neural Networks, San Sebastian, Spain, volume 4507 of Lecture Notes in Computer Science, pages 186–193. Springer Berlin / Heidelberg, June 2007. |
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79 | Elia Liitiäinen, Francesco Corona, and Amaury Lendasse. Non-parametric residual variance estimation in supervised learning. In Francisco Sandoval et al., editor, 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 of Lecture Notes in Computer Science, pages 63–71. Springer-Verlag, June 20-22 2007. |
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78 | Qi Yu, Eric Séverin, and Amaury Lendasse. Variable selection for financial modeling. In CEF 2007, 13th International Conference on Computing in Economics and Finance Montréal, Quebec, Canada, June 14 -16 2007. |
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77 | Tuomas Kärnä and Amaury Lendasse. Optimal gaussian basis functions for chemometrics. In SSC10, 10th Scandinavian Symposium on Chemometrics, Lappeenranta (Finland), page 79, June 11-15 2007. |
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76 | Amaury Lendasse and Francesco Corona. Optimal linear projection based on noise variance estimation: Application to spectrometric modeling. In Proceedings of SSC10 Scandinavian Symposium on Chemometrics, Lappeenranta (Finland), page 26, June 11-15 2007. |
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75 | Francesco Corona, Elia Liitiäinen, and Amaury Lendasse. Using functional representations in spectrophotoscopic variables selection and regression. In Proceedings of SSC10 Scandinavian Symposium on Chemometrics, Lappeenranta (Finland), page 29, June 11-15 2007. |
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74 | Yoan Miche, Patrick Bas, Amaury Lendasse, Christian Jutten, and Olli Simula. Extracting relevant features of steganographic schemes by feature selection techniques. In Wacha'07: Third Wavilla Challenge, Saint-Malo, France, June 14 2007. |
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73 | Qi Yu, Eric Séverin, and Amaury Lendasse. A global methodology for variable selection: Application to financial modeling. In Mashs 2007, Computational Methods for Modelling and learning in Social and Human Sciences, Brest (France), May 10-11 2007. |
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72 | Antti Sorjamaa, Paul Merlin, Bertrand Maillet, and Amaury Lendasse. A nonlinear approach for the determination of missing values in temporal databases. In MASHS, Computational Methods for Modelling and learning in Social and Human Sciences, Brest (France), May 10-11 2007. |
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71 | Elia Liitiäinen, Francesco Corona, and Amaury Lendasse. Nearest neighbor distributions and noise variance estimation. In M. Verleysen, editor, Proceedings of ESANN 2007, European Symposium on Artificial Neural Networks, Bruges (Belgium), pages 67–72. d-side publ. (Evere, Belgium), April 25-27 2007. |
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70 | Antti Sorjamaa, Paul Merlin, Bertrand Maillet, and Amaury Lendasse. SOM+EOF for finding missing values. In M. Verleysen, editor, ESANN 2007, European Symposium on Artificial Neural Networks, Bruges (Belgium), pages 115–120. d-side publ. (Evere, Belgium), April 25-27 2007. |
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69 | J. Vandewalle, J. Suykens, B. De Moor, and Amaury Lendasse. State-of-the-art and evolution in public data sets and competitions for system identification, time series prediction and pattern recognition. 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. |
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68 | Olli Simula, Amaury Lendasse, Francesco Corona, Satu-Pia Reinikainen, Marja-Liisa Riekkola, Kari Hartonen, Ilppo Vuorinen, and Jukka Silén. Developing chemometrics with the tools of information sciences (CHESS) – MASIT23. In MASI Programme 2005-2009, Yearbook 2007, pages 201–221. Libris Oy, March 2007. |
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67 | Nima Reyhani and Amaury Lendasse. An empirical dependence measures based on residual variance estimation. 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. |
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66 | Antti Sorjamaa and Amaury Lendasse. Time series prediction as a problem of missing values. In ESTSP 2007, European Symposium on Time Series Prediction, Espoo (Finland), pages 165–174, February 7-9 2007. |
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65 | Francesco Corona and Amaury Lendasse. Variable scaling for time series prediction. In Proceedings of ESTSP 2007, European Symposium on Time Series Prediction, Espoo (Finland), pages 69–76, February 7-9 2007. |
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64 | Tuomas Kärnä and Amaury Lendasse. Comparison of FDA based time series prediction methods. In ESTSP 2007, European Symposium on Time Series Prediction, Espoo (Finland), pages 77–86, February 7-9 2007. |
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63 | Amaury Lendasse, editor. ESTSP 2007: Proceedings. Multiprint Oy / Otamedia, 2007. ISBN: 978-951-22-8601-0. |
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2006 |
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62 | Yoan Miche, Benoit Roue, Patrick Bas, and Amaury Lendasse. A feature selection methodology for steganalysis. In B. Gunsel, A. K. Jain, A. M. Tekalp, and B. Sankur, editors, MRCS06, International Workshop on Multimedia Content Representation, Classification and Security, Istanbul (Turkey), volume 4105 of Lecture Notes in Computer Science, pages 49–56. Springer-Verlag, September 11-13 2006. |
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61 | Jarkko Tikka, Amaury Lendasse, and Jaakko Hollmén. Analysis of fast input selection: Application in time series prediction. In S. Kollias et al., editor, ICANN06, International Conference on Artificial Neural Networks, 16th International Conference, Athens (Greece), volume 4132/2006 of Lecture Notes in Computer Science, pages 161–170. Springer-Verlag, September 10-14 2006. |
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60 | Elia Liitiäinen and Amaury Lendasse. Long-term prediction of time series using state-space models. In S. Kollias et al., editor, ICANN'06, International Conference on Artificial Neural Networks, 16th International Conference, Athens (Greece), volume 4132/2006 of Lecture Notes in Computer Science, pages 181–190. Springer-Verlag, September 10-14 2006. |
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59 | Antti Sorjamaa and Amaury Lendasse. Time series prediction using dirrec strategy. In M. Verleysen, editor, ESANN06, European Symposium on Artificial Neural Networks, pages 143–148, Bruges, Belgium, April 26-28 2006. European Symposium on Artificial Neural Networks. |
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58 | Amaury Lendasse, Francesco Corona, Jin Hao, Nima Reyhani, and Michel Verleysen. Determination of the mahalanobis matrix using non-parametric noise estimations. In M. Verleysen, editor, Proceedings of ESANN 2006, European Symposium on Artificial Neural Networks, Bruges (Lille), pages 227–232, Bruges, Belgium, April 26-28 2006. |
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57 | Fabrice Rossi, Amaury Lendasse, Damien François, Vincent Wertz, and Michel Verleysen. Mutual information for the selection of relevant variables in spectrometric nonlinear modelling. Chemometrics and Intelligent Laboratory Systems, 80(2):215–226, February 2006. |
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2005 |
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56 | Amaury Lendasse, Damien François, Vincent Wertz, and Michel Verleysen. Nonparametric noise estimation to build nonlinear model in chemometry. In Chimiométrie 2005, Villeneuve d'Ascq (France), pages 143–146, November 30 - December 1 2005. |
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55 | Amaury Lendasse, Yongnan Ji, Nima Reyhani, and Michel Verleysen. LS-SVM hyperparameter selection with a nonparametric noise estimator. In W. Duch et al., editor, ICANN05, International Conference on Artificial Neural Networks, Artificial Neural Networks: Formal Models and Their Applications, volume 3697 of Lecture Notes in Computer Science, pages 625–630, September 11-15 2005. |
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54 | Antti Sorjamaa, Jin Hao, and Amaury Lendasse. Mutual information and k-nearest neighbors approximator for time series predictions. In W. Duch at al., editor, LNCS - Artificial Neural Networks: Formal Models and Their Applications - ICANN 2005, volume 3697/2005 of Lecture Notes in Computer Science, pages 553–558. Springer Berlin / Heidelberg, September 11-15 2005. |
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53 | Francesco Corona and Amaury Lendasse. Input selection and function approximation using the self-organizing map: An application to spectrometric modeling. In Proceedings of WSOM 2005 International Workshop on Self-Organizing Maps, Paris (France), pages 653–660, September 5-8 2005. |
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52 | Geoffroy Simon, Amaury Lendasse, Marie Cottrell, Jean-Claude Fort, and Michel Verleysen. Time series forecasting: Obtaining long term trends with self-organizing maps. Pattern Recognition Letters, 26(12):1795–1808, September 2005. |
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51 | Jarkko Tikka, Jaakko Hollmén, and Amaury Lendasse. Input selection for long-term prediction of time series. In Francisco Sandoval Joan Cabestany, Alberto Prieto, editor, Computational Intelligence and Bioinspired Systems: 8th International Workshop on Artificial Neural Networks, IWANN 2005, Vilanova i la Geltra, Barcelona, Spain, volume 3512 of Lecture Notes in Computer Science, pages 1002–1009. Springer-Verlag GmbH, June 2005. |
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50 | Antti Sorjamaa, Nima Reyhani, and Amaury Lendasse. Input and structure selection for k-NN approximator. In Francisco Sandoval Joan Cabestany, Alberto Prieto, editor, LNCS - Computational Intelligence and Bioinspired Systems - IWANN 2005, volume 3512/2005 of Lecture Notes in Computer Science, pages 985–992. Springer Berlin / Heidelberg, June 2005. |
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49 | Yongnan Ji, Jin Hao, Nima Reyhani, and Amaury Lendasse. Direct and recursive prediction of time series using mutual information selection. In J. Cabestany et al., editor, Computational Intelligence and Bioinspired Systems: 8th International Workshop on Artificial Neural Networks, IWANN'05, Vilanova i la Geltra, Barcelona, Spain, volume 3512 of Lecture Notes in Computer Science, pages 1010–1017. Springer-Verlag GmbH, June 8-10 2005. |
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48 | Nima Reyhani, Jin Hao, Yongnan Ji, and Amaury Lendasse. Mutual information and gamma test for input selection. In M. Verleysen, editor, ESANN 2005, European Symposium on Artificial Neural Networks, Bruges (Belgium), pages 503–508. d-side publ. (Evere, Belgium), April 27-29 2005. |
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47 | Antti Sorjamaa, Amaury Lendasse, and Michel Verleysen. Pruned lazy learning models for time series prediction. In M. Verleysen, editor, ESANN05, European Symposium on Artificial Neural Networks, pages 509–514. d-side publ. (Evere, Belgium), April 27-29 2005. |
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46 | Amaury Lendasse, Geoffroy Simon, Vincent Wertz, and Michel Verleysen. Fast bootstrap methodology for regression model selection. Neurocomputing, 64:161–181, March 2005. |
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2004 |
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45 | Amaury Lendasse, Damien François, Fabrice Rossi, Vincent Wertz, and Michel Verleysen. Sélection de variables spectrales par information mutuelle multivariée pour la construction de modèles non-linéaires. In Chimiométrie 2004, Paris (France), pages 44–47, November 30 - December 1 2004. |
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44 | Antti Sorjamaa, Amaury Lendasse, Damien François, and Michel Verleysen. Business plans classification with locally pruned lazy learning models. In ACSEG 2004, Connectionist Approaches in Economics and Management Sciences, Lille (France), pages 112–119, November 18-19 2004. |
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43 | Geoffroy Simon, Amaury Lendasse, Marie Cottrell, Jean-Claude Fort, and Michel Verleysen. Double quantization of the regressor space for long-term time series prediction: Method and proof of stability. Neural Networks, 17(8-9):1169–1181, October-November 2004. Special Issue. |
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42 | Amaury Lendasse, Vincent Wertz, Geoffroy Simon, and Michel Verleysen. Fast bootstrap applied to LS-SVM for long term prediction of time series. In Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on, volume 1, pages 705–710. IEEE, July 2004. |
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41 | Amaury Lendasse, Erkki Oja, Olli Simula, and Michel Verleysen. Time series prediction competition: The CATS benchmark. In IJCNN 2004, International Joint Conference on Neural Networks, volume 2, pages 1615–1620, Budapest, Hungary, July, 25-29 2004. |
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40 | Amaury Lendasse, Geoffroy Simon, Robert Kozma, Vincent Wertz, and Michel Verleysen. Fast bootstrap for least-square support vector machines. In M. Verleysen, editor, ESANN 2004, European Symposium on Artificial Neural Networks, Bruges (Belgium), pages 525–530. d-side publ. (Evere, Belgium), April 28-30 2004. |
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39 | John A. Lee, Amaury Lendasse, and Michel Verleysen. Nonlinear projection with curvilinear distances: Isomap versus curvilinear distance analysis. Neurocomputing, 57:49–76, March 2004. |
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38 | Eric de Bodt, Amaury Lendasse, Pierre Cardon, and Michel Verleysen. Self-organizing feature maps for the classification of investment funds. Journal of Economic and Social Systems, 17(1-2):183–195, 2004. |
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37 | Igor Beliaev, Robert Kozma, and Amaury Lendasse. Robust time series prediction using KIII model. In IDS04 Symposium, FedEx Institute of Technology (FIT), University of Memphis, TN, USA, pages April 24–26, Published 2004. |
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2003 |
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36 | Simon Dablemont, Geoffroy Simon, Amaury Lendasse, Alain Ruttiens, and Michel Verleysen. Financial time series forecasting by double SOM maps and local RBF models forecasting the DAX30 index. In ACSEG 2003, Rencontre Internationale sur les Approches Connexionnistes en Sciences Economiques et de Gestion, Nantes (France), pages 153–164, November 20-21 2003. |
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35 | Damien François, Amaury Lendasse, Benoit Gailly, Vincent Wertz, and Michel Verleysen. Are business plans usefull for investors ?. In ACSEG 2003, Connectionist Approaches in Economics and Management Sciences, Nantes (France), pages 239–249, November 20-21 2003. |
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34 | Michel Verleysen and Amaury Lendasse. Le test des méthodes neuronales – ou comment utiliser les techniques de rééchantillonnage pour ne pas se tromper de résultat. In ACSEG 2003 proceedings - Connectionist Approaches in Economics and Management Sciences, Nantes (France), pages 515–534, November 20-21 2003. |
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33 | Simon Dablemont, Geoffroy Simon, Amaury Lendasse, Alain Ruttiens, François Blayo, and Michel Verleysen. Time series forecasting with SOM and local non-linear models - application to the DAX30 index prediction. In Proceedings of the Workshop on Self-organizing Maps, pages 340–345, Hibikino, Japan, September 11-14 2003. |
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32 | Geoffroy Simon, Amaury Lendasse, Marie Cottrell, Jean-Claude Fort, and Michel Verleysen. Double SOM for long-term time series prediction. In WSOM 2003, Workshop on Self-Organizing Maps, pages 35–40, Hibikino, Japan, September 11-14 2003. |
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31 | Geoffroy Simon, Amaury Lendasse, Marie Cottrell, and Michel Verleysen. Long-term time series forecasting using self-organizing maps: the double vector quantization method. In ANNPR 2003, Artificial Neural Networks in Pattern Recognition, Florence (Italy), pages 8–14, September 12-13 2003. |
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30 | Geoffroy Simon, Amaury Lendasse, and Michel Verleysen. Bootstrap for model selection: Linear approximation of the optimism. In J.R. Alvarez J. Mira, editor, IWANN 2003, International Work-Conference on Artificial and Natural Neural Networks, Mao, Menorca (Spain), volume 2686–1 of Lecture Notes in Computer Science, pages 182–189. Springer-Verlag, June 3-6 2003. |
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29 | Amaury Lendasse, Vincent Wertz, and Michel Verleysen. Model selection with cross-validations and bootstraps - application to time series prediction with RBFN models. In O. Kaynak, E. Alpaydin, E. Oja, and L. Xu, editors, ICANN 2003, Joint International Conference on Artificial Neural Networks, Istanbul (Turkey), volume 2714 of Lecture Notes in Computer Science, pages 573–580. Springer-Verlag, June 26-29 2003. |
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28 | Geoffroy Simon, Amaury Lendasse, Vincent Wertz, and Michel Verleysen. Fast approximation of the bootstrap for model selection. In M. Verleysen, editor, ESANN 2003, European Symposium on Artificial Neural Networks, Bruges (Belgium), pages 99–106. d-side publ. (Evere, Belgium), April 23-25 2003. |
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27 | Damien François, Benoit Gailly, Amaury Lendasse, Vincent Wertz, and Michel Verleysen. Should seed investors read business plans?. In 22th Benelux Meeting on Systems and Control, Lommel, Belgium, March 19-21 2003. |
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26 | Amaury Lendasse, Geoffroy Simon, Vincent Wertz, and Michel Verleysen. Fast bootstrap for model structure selection. In 22th Benelux Meeting on Systems and Control, Lommel, Belgium, page 81, March 19-21 2003. |
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25 | Amaury Lendasse, Damien François, Vincent Wertz, and Michel Verleysen. Nonlinear time series prediction by weighted vector quantization. In P.M.A. Sloot et al., editor, Computational Science — ICCS 2003, volume 2657–1 of Lecture Notes in Computer Science, pages 417–426. Springer Berlin / Heidelberg, January 2003. |
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24 | Amaury Lendasse, John A. Lee, Eric de Bodt, Vincent Wertz, and Michel Verleysen. Approximation by Radial-Basis Function networks - Application to option pricing, volume 6 of Advances in Computational Management Science, C. Lesage and M. Cottrell editors, chapter 10 in Connectionist Approaches in Economics and Management Sciences, pages 203–214. Kluwer academic, 2003. |
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23 | Amaury Lendasse. Analyse et prédiction de séries temporelles par méthodes non linéaires: Application à des données industrielles et financières. PhD thesis, Université catholique de Louvain, 2003. |
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2002 |
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22 | Pierre Cardon, Amaury Lendasse, Vincent Wertz, Eric de Bodt, and Michel Verleysen. Classification of investment funds by self-organizing maps. In ACSEG 2002, Connectionist Approaches in Economics and Management Sciences, Boulogne-sur-Mer (France), pages 201–212, November 21-22 2002. |
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21 | Amaury Lendasse, John A. Lee, Vincent Wertz, and Michel Verleysen. Forecasting electricity consumption using nonlinear projection and self-organizing maps. Neurocomputing, 48(1-4):299–311, October 2002. |
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20 | Amaury Lendasse, Marie Cottrell, Vincent Wertz, and Michel Verleysen. Prediction of electric load using kohonen maps - application to the polish electricity consumption. In ACC 2002, American Control Conference, Anchorage, Alaska (USA), pages 3684–3689, June 2002. |
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19 | Amaury Lendasse and Michel Verleysen. Curvilinear distance analysis versus isomap. In M. Verleysen, editor, ESANN 2002, European Symposium on Artificial Neural Networks, Bruges (Belgium), pages 185–192. d-side publ. (Evere, Belgium), April 2002. |
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18 | Nabil Benoudjit, Cédric Archambeau, Amaury Lendasse, John A. Lee, and Michel Verleysen. Width optimization of the gaussian kernels in radial basis function networks. In M. Verleysen, editor, ESANN 2002, European Symposium on Artificial Neural Networks, Bruges (Belgium), pages 425–432. d-side publ. (Evere, Belgium), April 2002. |
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2001 |
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17 | Cédric Archambeau, Amaury Lendasse, Charles Trullemans, Claude Veraart, Jean Delbeke, and Michel Verleysen. Phosphene evaluation in a visual prosthesis with artificial neural networks. In Adaptive Systems and Hybrid Computational Intelligence in Medicine, special session proceedings of EUNITE 2001, Tenerife (Spain), pages 116–122, December 13-14 2001. |
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16 | Cédric Archambeau, Amaury Lendasse, Charles Trullemans, Claude Veraart, Jean Delbeke, and Michel Verleysen. Phosphene evaluation in a visual prosthesis with artificial neural networks. 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. |
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15 | Amaury Lendasse, John A. Lee, Eric de Bodt, Vincent Wertz, and Michel Verleysen. Approximation using radial basis functions networks - application to pricing derivative securities. In ACSEG 2001, Connectionist Approaches in Economics and Management Sciences, Rennes (France), pages 275–283, November 22-23 2001. |
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14 | Amaury Lendasse, John A. Lee, Eric de Bodt, Vincent Wertz, and Michel Verleysen. Input data reduction for the prediction of financial time series. In M. Verleysen, editor, ESANN 2001, European Symposium on Artificial Neural Networks, Bruges (Belgique), pages 237–244. d-side publ. (Evere, Belgium), April 2001. |
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13 | Amaury Lendasse, Vincent Wertz, and Michel Verleysen. Forecasting electricity demand using kohonen maps. In 20th Benelux meeting on Systems and Control, Houffalize (Belgium), page 118, March 2001. |
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12 | Amaury Lendasse, Eric de Bodt, Vincent Wertz, and Michel Verleysen. Nonlinear financial time series forecasting - application to the bel 20 stock market index. European Journal of Economic and Social Systems, 14(1):81–92, February 2001. |
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11 | Amaury Lendasse, John A. Lee, Vincent Wertz, Eric de Bodt, and Michel Verleysen. Dimension reduction of technical indicators for the prediction of financial time series, application to the bel 20 market index. European Journal of Economic and Social Systems, 15(2):31–48, 2001. |
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2000 |
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10 | Amaury Lendasse, John A. Lee, Eric de Bodt, Vincent Wertz, and Michel Verleysen. 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. In ACSEG 2000, 7emes rencontres internationales, December 2000. |
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9 | John A. Lee, Amaury Lendasse, N. Donckers, and Michel Verleysen. A robust non-linear projection method. In M. Verleysen, editor, ESANN'2000, European Symposium on Artificial Neural Networks, Bruges (Belgique), pages 13–20, April 2000. |
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8 | Amaury Lendasse, John A. Lee, Vincent Wertz, and Michel Verleysen. Time series forecasting using CCA and kohonen maps - application to electricity consumption. In M. Verleysen, editor, ESANN'2000, European Symposium on Artificial Neural Networks, Bruges (Belgique), pages 329–334, April 2000. |
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7 | G. Gomez and Amaury Lendasse. Statistical fault isolation with PCA. In IFAC, Safeprocess', 2000. |
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1999 |
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6 | Michel Verleysen, Eric de Bodt, and Amaury Lendasse. Forecasting financial time series through intrinsic dimension estimation and non-linear data projection. In J. Sanchez-Andres J. Mira, editor, 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 of Lecture Notes in Computer Science, pages 596–605. Springer-Verlag, June 1999. |
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5 | N. Donckers, Amaury Lendasse, Vincent Wertz, and Michel Verleysen. Extraction of intrinsic dimension using CCA - application to blind sources separation. In ESANN'99, European Symposium on Artificial Neural Networks, Bruges (Belgique), pages 339–344, April 1999. |
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4 | Amaury Lendasse. Comparison between NAR and NARMA models for time-series prediction: Choice of a non-linear regressor vector. In 18th Benelux Meeting on Systems and Control, Conference Center "Hengelhoef", Houthalen, Belgium, March 3-5 1999. |
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1998 |
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3 | Amaury Lendasse, Eric de Bodt, and Michel Verleysen. Estimation de la dimension intrinsèque d'une série temporelle et prédiction par une méthode de projection. In ACSEG'98, Association Connectioniste en Sciences Economiques et de Gestion, Louvain-la-Neuve (Belgique), pages D37–D46, November 20 1998. |
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2 | M. L. Hadjili, Amaury Lendasse, Vincent Wertz, and S. Yurkovich. Identification of fuzzy models for a glass furnace process. In 1998 IEEE International Conference on Control Applications,Trieste, Italy, pages 963–968, September 1-4 1998. |
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1 | Amaury Lendasse, Michel Verleysen, Eric de Bodt, Marie Cottrell, and P. Gregoire. Forecasting time-series by kohonen classification. In M. Verleysen, editor, ESANN'98, European Symposium on Artificial Neural Networks, Bruges (Belgique), pages 221–226, April 1998. |
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