2010 |
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33 | Antti Sorjamaa. Methodologies for Time Series Prediction and Missing Value Imputation. PhD thesis, Aalto University School of Science and Technology, November 2010. |
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32 | Dušan Sovilj, Antti Sorjamaa, Qi Yu, Yoan Miche, and Eric Séverin. OPELM and OPKNN in long-term prediction of time series using projected input data. Neurocomputing, 73(10-12):1976–1986, June 2010. |
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31 | Souhaib Ben Taieb, Antti Sorjamaa, and Gianluca Bontempi. Multiple-output modelling for multi-step-ahead time series forecasting. Neurocomputing, 73(10-12):1950–1957, June 2010. |
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30 | 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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29 | 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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28 | 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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27 | 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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26 | 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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25 | 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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24 | 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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23 | 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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22 | 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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21 | 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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20 | 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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19 | 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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18 | 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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2008 |
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17 | 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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16 | 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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15 | Dušan Sovilj, Antti Sorjamaa, and Yoan Miche. Tabu search with delta test for time series prediction using OP-KNN. In Amaury Lendasse, editor, ESTSP, European Symposium on Time Series Prediction, pages 187–196, Porvoo, Finland, September 17-19 2008. Multiprint Oy / Otamedia , Espoo, Finland. |
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14 | Qi Yu, Antti Sorjamaa, Yoan Miche, and Eric Séverin. A methodology for time series prediction in finance. In Amaury Lendasse, editor, ESTSP, European Symposium on Time Series Prediction, pages 285–293, Porvoo, Finland, September 17-19 2008. Multiprint Oy / Otamedia , Espoo, Finland. |
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13 | 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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12 | 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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2007 |
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11 | 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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10 | 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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9 | 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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8 | 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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7 | 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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2006 |
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6 | 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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2005 |
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5 | Antti Sorjamaa. Strategies for the long-term prediction of time series using local models. Master's thesis, Helsinki University of Technology, October 14 2005. Master Thesis obtained with the grade 5. |
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4 | 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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3 | 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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2 | 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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2004 |
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1 | Antti Sorjamaa, Amaury Lendasse, Damien Francois, 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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