Jaakko Peltonen

I am an academy research fellow and docent (adjunct professor) at Aalto University, Department of Information and Computer Science, where I am a PI of the Statistical Machine Learning and Bioinformatics research group.

I am also an associate professor of statistics (data analysis) at University of Tampere, School of Information Sciences. I have a corresponding webpage at University of Tampere.

I previously worked at University of Sheffield, Sheffield Institute for Translational Neuroscience, with Prof. Magnus Rattray and Prof. Neil Lawrence. I am a member of the Centre of Excellence in Computational Inference Research COIN, Helsinki Institute for Information Technology HIIT, and the PASCAL2 Network of Excellence. My research interests include probabilistic generative and information-theoretic methods and formalisms such as information retrieval based dimensionality reduction, especially for application in visualization, clustering, and bioinformatics.

Contact information


Mail address: Aalto University, Department of Information and Computer Science,
P.O. Box 15400, FI-00076 Aalto, Finland
E-Mail:firstname dot lastname at aalto dot fi
Phone:+358 50 3623628

Positions of Trust and Expertise

I am an associate editor of Neural Processing Letters, and recently co-edited a special issue of Neurocomputing on machine learning for signal processing. I have been a program committee member for 24 conferences/workshops: DaWaK 2005-2008, the NIPS 2008 Learning from Multiple Sources Workshop, ISNN 2009, the NIPS 2009 workshop on Learning from Multiple Sources with Applications to Robotics, ISNN 2010, MLSP 2010, ISNN 2011, WSOM 2011, ICANN 2011, CIP 2012, MLSP 2012, ICANN 2012, ISNN 2012, the NC2 2012, 2013, and 2014 workshops, VAMP 2013, ICANN 2013, WSOM 2014, ESANN 2014, and ICANN 2014. I have been the publicity chair for AISTATS 2014, SCIA 2013, ICANN 2011 and WSOM 2011, the local publicity chair for MLSP 2010, and an organizer of the NIPS 2009 workshop on Learning from Multiple Sources with Applications to Robotics, and publicity chair for the MLSS 2014 Iceland Machine Learning Summer School. I have reviewed for several journals listed below, and for 61 conferences so far (e.g. NIPS, ICML, ECML PKDD, MLSP, ICANN, ICASSP, DaWaK, Web Intelligence).

Reviewed for these journals: IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Neural Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE/ACM Transactions on Computational Biology and Bioinformatics, International Journal of Neural Systems, Neural Processing Letters, IEEE Signal Processing Letters, Journal of Machine Learning Research, Neural Networks, Pattern Analysis and Applications, Neurocomputing, Machine Learning, Pattern Recognition, Information Processing & Management, PLoS ONE, IEEE Transactions on Systems, Man, and Cybernetics, part B, IEEE Transactions on Image Processing, Information Sciences, Data Mining and Knowledge Discovery, The Computer Journal

Teaching

In Spring 2014 I lecture the course T-61.2020 From Data to Knowledge Project Assignment with Amaury Lendasse at Aalto. I also lecture the course MTTS1 Dimensionality reduction and visualization and the course MTTTS2 Pro gradu thesis and seminar at University of Tampere.

Earlier teaching:
In Spring 2013 lectured the course T-61.5010 Information Visualization together with Francesco Corona, and the course T-61.2020 From Data to Knowledge Project Assignment. In Fall 2013 I lectured the course T-61.3050 Machine Learning: Basic Principles together with Ritabrata Dutta, and the special course T-61.6040 Advanced Course in Information Visualization together with Kerstin Bunte and Manuel Eugster. In Autumn 2012 I lectured the special course T-61.6040 Multi-view and Multi-task Learning together with Sohan Seth. In Autumn 2010 I lectured two courses at Aalto University: T-61.3040 Statistical Modeling of Signals (also several earlier semesters; 2010 with Amaury Lendasse) and T-61.6040 Learning from Multiple Sources (with Arto Klami). Earlier I lectured the course 582480 Machine Learning in Bioinformatics at University of Helsinki. Still earlier I was course assistant for several years on the course T-61.5030 Advanced Course in Neural Computing at Helsinki University of Technology.

Students

Alumni:


Publications

Journal Publications


  1. Jaakko Peltonen and Ziyuan Lin. Information Retrieval Approach to Meta-visualization. Machine Learning, accepted for publication. (online-first article on publisher webpages)

  2. Joni Pajarinen, Ari Hottinen, and Jaakko Peltonen. Optimizing spatial and temporal reuse in wireless networks by decentralized partially observable Markov decision processes. IEEE Transactions on Mobile Computing, 13(4):866-879, 2014. (pdf on publisher pages)

  3. Ali Faisal*, Jussi Gillberg, Gayle Leen, and Jaakko Peltonen*. Transfer Learning using a Nonparametric Sparse Topic Model. Neurocomputing, 112:124-137, 2013. (* equal contributions) (final pdf on publisher pages)

  4. Gayle Leen*, Jaakko Peltonen*, and Samuel Kaski. Focused multi-task learning in a Gaussian process framework. Machine Learning, 89(1-2):157-182, 2012. (* equal contributions) (final pdf on publisher pages)

  5. Samuel Kaski and Jaakko Peltonen. Dimensionality Reduction for Data Visualization. IEEE Signal Processing Magazine, 28(2):100-104, 2011. (preprint pdf, final pdf on publisher pages)

  6. Joni Pajarinen, Jaakko Peltonen, and Mikko A. Uusitalo. Fault tolerant machine learning for nanoscale cognitive radio. Neurocomputing, 74(5):753-764, 2011. (preprint pdf, final version on publisher pages)

  7. Mikko A. Uusitalo, Jaakko Peltonen, and Tapani Ryhänen. Machine Learning: How It Can Help Nanocomputing. Journal of Computational and Theoretical Nanoscience, 8:1347-1363, 2011. (final pdf on publisher pages)

  8. Jarkko Venna, Jaakko Peltonen, Kristian Nybo, Helena Aidos, and Samuel Kaski. Information retrieval perspective to nonlinear dimensionality reduction for data visualization. Journal of Machine Learning Research, 11:451-490, 2010. (abstract, preprint pdf, final pdf at JMLR)

  9. Jaakko Peltonen, Yusuf Yaslan, and Samuel Kaski. Relevant subtask learning by constrained mixture models. Intelligent Data Analysis, 14:641-662, 2010. (abstract, preprint pdf, final version on publisher pages)

  10. Jaakko Peltonen, Jarkko Venna, and Samuel Kaski. Visualizations for Assessing Convergence and Mixing of Markov Chain Monte Carlo Simulations. Computational Statistics and Data Analysis, 53:4453-4470, 2009. (abstract, preprint pdf, final version on publisher pages) © Elsevier B. V.

  11. Merja Oja, Jaakko Peltonen, Jonas Blomberg and Samuel Kaski. Methods for estimating human endogenous retrovirus activities from EST databases. BMC Bioinformatics, 8 (Suppl 2): S11, 2007. (html)

  12. Jaakko Peltonen and Samuel Kaski. Discriminative Components of Data. IEEE Transactions on Neural Networks, 16:68-83, 2005. (preprint abstract, preprint pdf, final paper on IEEE pages)

  13. Jaakko Peltonen, Arto Klami, and Samuel Kaski. Improved Learning of Riemannian Metrics for Exploratory Data Analysis. Neural Networks, vol. 17, pages 1087-1100, 2004. (preprint abstract, preprint gzipped postscript, preprint pdf, final paper on Elsevier pages, erratum to final paper on Elsevier pages)

  14. Samuel Kaski, Janne Sinkkonen, and Jaakko Peltonen. Bankruptcy analysis with self-organizing maps in learning metrics. IEEE Transactions on Neural Networks, 12:936-947, 2001. (preprint abstract, preprint gzipped postscript, preprint pdf, final paper on IEEE pages)

Refereed International Conference Publications


  1. Zhirong Yang, Jaakko Peltonen, and Samuel Kaski. Optimization Equivalence of Divergences Improves Neighbor Embedding. In Proceedings of ICML 2014, The 31st International Conference on Machine Learning, 2014. (final pdf on publisher pages, supplemental document, code)

  2. Kerstin Bunte, Matti Järvisalo, Jeremias Berg, Petri Myllymäki, Jaakko Peltonen and Samuel Kaski. Optimal Neighborhood Preserving Visualization by Maximum Satisfiability. In Proceedings of AAAI-14, The Twenty-Eighth AAAI Conference on Artificial Intelligence, to appear. (preprint pdf, final pdf on publisher pages)

  3. Jaakko Peltonen and Ziyuan Lin. Information Retrieval Perspective to Meta-visualization. In Cheng Soon Ong and Tu Bao Ho, editors, Proceedings of ACML 2013, Fifth Asian Conference on Machine Learning, JMLR W&CP 29:165-180, 2013. (final pdf on publisher pages)

  4. Tuukka Ruotsalo*, Jaakko Peltonen*, Manuel Eugster*, Dorota Glowacka, Ksenia Konyushkova, Kumaripaba Athukorala, Ilkka Kosunen, Aki Reijonen, Petri Myllymäki, Giulio Jacucci, and Samuel Kaski. Directing Exploratory Search with Interactive Intent Modeling. In Proceedings of CIKM 2013, ACM Conference on Information and Knowledge Management, pages 1759-1764. ACM, 2013. (* equal contributions) (preprint pdf, final pdf on publisher pages)

  5. Jaakko Peltonen and Ziyuan Lin. Multiplicative Update For Fast Optimization Of Information Retrieval Based Neighbor Embedding. In Saeid Sanei, Paris Smaragdis, Asoke Nandi, Anthony TS Ho, and Jan Larsen, editors, Proceedings of MLSP 2013, IEEE International Workshop on Machine Learning for Signal Processing, IEEE, 2013. (final pdf on publisher pages)

  6. Joni Pajarinen and Jaakko Peltonen. Expectation maximization for average reward decentralized POMDPs. In Proceedings of ECML PKDD 2013, The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, pages 129-144. Springer, 2013. (final paper on publisher pages)

  7. Jaakko Peltonen, Max Sandholm, and Samuel Kaski. Information Retrieval Perspective to Interactive Data Visualization. In Proceedings of Eurovis 2013, the Eurographics Conference on Visualization - short papers, pages 49-53. European Association for Computer Graphics, 2013. (preprint pdf, final paper on publisher pages)

  8. Zhirong Yang, Jaakko Peltonen, and Samuel Kaski. Scalable Optimization of Neighbor Embedding for Visualization. In Proceedings of ICML 2013, International Conference on Machine Learning, JMLR W&CP 28(2):127-135, 2013. (final pdf on publisher pages)

  9. Jaakko Peltonen and Konstantinos Georgatzis. Efficient Optimization for Data Visualization as an Information Retrieval Task. In Ignacio Santamaria, Jerónimo Arenas-García, Gustavo Camps-Valls, Deniz Erdogmus, Fernando Pérez-Cruz, and Jan Larsen, editors, Proceedings of MLSP 2012, the 2012 IEEE International Workshop on Machine Learning for Signal Processing. IEEE, 2012. (preprint pdf, final paper on IEEE pages)

  10. Ali Faisal*, Jussi Gillberg*, Jaakko Peltonen*, Gayle Leen, and Samuel Kaski. Sparse Nonparametric Topic Model for Transfer Learning. In Proceedings of ESANN 2012, 20th European Symposium on Artificial Neural Networks, ESANN, 2012. (* equal contributions) (preprint pdf, final pdf on publisher pages)

  11. Joni Pajarinen and Jaakko Peltonen. Periodic Finite State Controllers for Efficient POMDP and DEC-POMDP Planning. In J. Shawe-Taylor, R. S. Zemel, P. Bartlett, F. C. N. Pereira, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 24 (Proceedings of NIPS 2011), pages 2636-2644, 2011. (PDF on publisher pages)

  12. Gayle Leen, Jaakko Peltonen, and Samuel Kaski. Focused Multi-task Learning Using Gaussian Processes. In Dimitrios Gunopulos, Thomas Hofmann, Donato Malerba, and Michalis Vazirgiannis, editors, Machine Learning and Knowledge Discovery in Databases (proceedings of ECML PKDD 2011), Part II, pages 310-325. Springer-Verlag, Berlin Heidelberg, 2011. Winner of the ECML PKDD 2011 Best Paper Award in Machine Learning. (preprint PDF, final PDF on publisher pages)

  13. Joni Pajarinen and Jaakko Peltonen. Efficient planning for factored infinite-horizon DEC-POMDPs. In Proceedings of IJCAI-11, the 22nd International Joint Conference on Artificial Intelligence, pages 325-331. AAAI Press, 2011. (abstract, final PDF on publisher pages)

  14. Jaakko Peltonen and Samuel Kaski. Generative Modeling for Maximizing Precision and Recall in Information Visualization. In Geoffrey Gordon, David Dunson, and Miroslav Dudik, eds., Proceedings of AISTATS 2011, the 14th International Conference on Artificial Intelligence and Statistics. JMLR W&CP, vol. 15, 2011. (abstract, final pdf on publisher pages, supplementary information)

  15. Joni Pajarinen, Jaakko Peltonen, Ari Hottinen, and Mikko A. Uusitalo. Efficient Planning in Large POMDPs through Policy Graph Based Factorized Approximations. In Proceedings of ECML PKDD 2010, part III, pages 1-16. Springer-Verlag, Berlin Heidelberg, 2010. (preprint pdf, final version on publisher pages)

  16. Juuso Parkkinen, Kristian Nybo, Jaakko Peltonen, and Samuel Kaski. Graph Visualization With Latent Variable Models. In Proceedings of MLG 2010, the Eighth Workshop on Mining and Learning with Graphs, pages 94-101. ACM, New York, NY, USA, 2010. (preprint pdf, final version on publisher pages)

  17. Jaakko Peltonen, Helena Aidos, Nils Gehlenborg, Alvis Brazma, and Samuel Kaski. An information retrieval perspective on visualization of gene expression data with ontological annotation. In Proceedings of ICASSP 2010, pages 2178-2181. IEEE, 2010. (abstract, preprint pdf, final version on publisher pages)

  18. Joni Pajarinen, Jaakko Peltonen, Mikko A. Uusitalo, and Ari Hottinen. Latent state models of primary user behavior for opportunistic spectrum access. In Proceedings of the 20th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC'09), pages 1267-1271. IEEE, 2009. (preprint pdf, final version on publisher pages. Note: work was also supported by Academy of Finland decision 123983.)

  19. Jaakko Peltonen. Visualization by Linear Projections as Information Retrieval. In José Príncipe and Risto Miikkulainen, editors, Advances in Self-Organizing Maps (proceedings of WSOM 2009), pages 237-245. Springer, Berlin Heidelberg, 2009. (abstract, preprint pdf, final paper on Springer pages) © Springer-Verlag Berlin Heidelberg 2009

  20. Jaakko Peltonen, Helena Aidos, and Samuel Kaski. Supervised Nonlinear Dimensionality Reduction by Neighbor Retrieval. In Proceedings of the IEEE 2009 International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2009), pages 1809-1812. IEEE, 2009. (abstract, preprint pdf, final paper on publisher pages)

  21. Jaakko Peltonen, Mikko A. Uusitalo, and Joni Pajarinen. Nano-scale Fault Tolerant Machine Learning for Cognitive Radio. In proceedings of the 2008 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2008), pages 163-168. IEEE, 2008. (final paper on IEEE pages)

  22. Samuel Kaski and Jaakko Peltonen. Learning from Relevant Tasks Only. In Joost N. Kok, Jacek Koronacki, Ramon Lopez de Mantaras, Stan Matwin, Dunja Mladenic, and Andrzej Skowron, editors, Machine Learning: ECML 2007 (Proceedings of the 18th European Conference on Machine Learning), Lecture Notes in Artificial Intelligence 4701, pages 608-615. Springer-Verlag, Berlin, Germany, 2007. (abstract, preprint pdf, final paper on Springer pages) © 2007 Springer-Verlag.

  23. Jaakko Peltonen, Jacob Goldberger, and Samuel Kaski. Fast Semi-supervised Discriminative Component Analysis. In Konstantinos Diamantaras, T?ay Adali, Ioannis Pitas, Jan Larsen, Theophilos Papadimitriou, and Scott Douglas, editors, Machine Learning for Signal Processing XVII, pages 312-317. IEEE, 2007. (abstract, preprint pdf, final paper on publisher pages)

  24. Merja Oja, Jaakko Peltonen, and Samuel Kaski. Estimation of human endogenous retroviruses from sequence databases. In Juho Rousu, Samuel Kaski, and Esko Ukkonen, editors, Probabilistic Modeling and Machine Learning in Structural and Systems Biology (PMSB 2006), workshop proceedings, pages 50-54, Helsinki University Printing House, 2006. (abstract, gzipped postscript, pdf)

  25. Jaakko Peltonen, Janne Sinkkonen, and Samuel Kaski. Sequential Information Bottleneck for Finite Data. In Russ Greiner and Dale Schuurmans, editors, Proceedings of the Twenty-First International Conference on Machine Learning (ICML 2004), pp. 647-654, Omnipress, Madison, WI, 2004. (abstract, pdf, final paper on publisher pages)

  26. Jaakko Peltonen, Arto Klami, and Samuel Kaski. Learning Metrics for Information Visualization. In Proceedings of the Workshop on Self-Organizing Maps (WSOM'03), Hibikino, Kitakyushu, Japan, September 2003. pp. 213-218. (abstract, postscript, gzipped postscript, pdf)

  27. Samuel Kaski and Jaakko Peltonen. Informative discriminant analysis. In: Tom Fawcett and Nina Mishra, editors, Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), pp. 329-336, AAAI Press, Menlo Park, CA, 2003. (abstract, gzipped postscript, pdf)

  28. Jarkko Venna, Samuel Kaski, and Jaakko Peltonen. Visualizations for Assessing Convergence and Mixing of MCMC. N. Lavrac, D. Gamberger, H. Blockeel, L. Todorovski, editors, Proceedings of the 14th European Conference on Machine Learning (ECML 2003), pp. 432-443. Springer, Berlin, 2003. (abstract, postscript, gzipped postscript, pdf)

  29. Jaakko Peltonen, Janne Sinkkonen, and Samuel Kaski. Discriminative clustering of text documents. In: Lipo Wang, Jagath C. Rajapakse, Kunihiko Fukushima, Soo-Young Lee, Xin Yao (eds.) Proceedings of ICONIP'02, 9th International Conference on Neural Information Processing, volume 4, pages 1956-1960. IEEE, Piscataway, NJ, 2002. (abstract, postscript, gzipped postscript, pdf)

  30. Jaakko Peltonen, Arto Klami, and Samuel Kaski. Learning More Accurate Metrics for Self-Organizing Maps. In Jos?R. Dorronsoro, editor, Artificial Neural Networks - ICANN 2002, International Conference, Madrid, Spain, August 2002, Proceedings, pp. 999-1004. Springer, 2002. (abstract, postscript, gzipped postscript, pdf) © Springer-Verlag

  31. Samuel Kaski, Janne Sinkkonen, and Jaakko Peltonen. Learning metrics for self-organizing maps. In Proceedings of IJCNN01, International Joint Conference on Neural Networks, pages 914-919. IEEE, Piscataway, NJ, 2001. (abstract, postscript, gzipped postscript, pdf)

  32. Samuel Kaski, Janne Sinkkonen, and Jaakko Peltonen. Data visualization and analysis with self-organizing maps in learning metrics. In Y. Kambayashi, W. Winiwarter, M. Arikawa, eds., Proceedings of DaWak'01, Third International Conference on Data Warehousing and Knowledge Discovery, pages 162-173. Springer, Berlin, 2001. (pdf at Springer pages)

Refereed Finnish Conference Publications


  1. Arto Klami, Jaakko Peltonen, and Samuel Kaski. Accurate self-organizing maps in learning metrics. In Pekka Ala-Siuru and Samuel Kaski, editors, Step 2002 -- Intelligence, The Art of Natural and Artificial, pages 41-49. Finnish Artificial Intelligence Society, 2002.

Publications as Editor


  1. Jaakko Peltonen, Tapani Raiko, and Samuel Kaski (editors). Machine learning for signal processing 2010, Neurocomputing vol. 80, 2012. (volume on publisher pages)

Other Scientific Publications


  1. Jaakko Peltonen, Ali Faisal, Elisabeth Georgii, Johan Rung and Samuel Kaski. Toward computational cumulative biology by combining models of biological datasets. In NIPS 2014 Workshop on Machine Learning in Computational Biology, accepted.

  2. Tuukka Ruotsalo, Jaakko Peltonen, Manuel J. A. Eugster, Dorota Glowacka, Ksenia Konyushkova, Kumaripaba Athukorala, Ilkka Kosunen, Aki Reijonen, Petri Myllymaki, Giulio Jacucci, Samuel Kaski. Bayesian Optimization in Interactive Scientific Search. In NIPS 2014 Workshop on Bayesian Optimization in Academia and Industry, accepted.

  3. Antti Kangasrääsiö, Dorota Glowacka, Tuukka Ruotsalo, Jaakko Peltonen, Manuel J. A. Eugster, Ksenia Konyushkova, Kumaripaba Athukorala, Ilkka Kosunen, Aki Reijonen, Petri Myllymäki, Giulio Jacucci, and Samuel Kaski. Interactive Visualization of Search Intent for Exploratory Information Retrieval. In ICML 2014 Workshop on Crowdsourcing and Human Computing, 2014.

  4. Ali Faisal, Jaakko Peltonen, Elisabeth Georgii, Johan Rung, and Samuel Kaski. Toward computational cumulative biology by combining models of biological datasets. ArXiv preprint, arXiv:1404.0329v1 [q-bio.QM], 2014.

  5. Tuukka Ruotsalo, Jaakko Peltonen, Aki Reijonen, Giulio Jacucci, Manuel J.A. Euster, and Samuel Kaski. IntentRadar: Interactive Search User Interface that Anticipates User's Search Intents. Refereed extended abstract (4 pages) in CHI Interactivity 2014, 2014.

  6. Tuukka Ruotsalo, Jaakko Peltonen, Manuel J. A. Eugster, Dorota Glowacka, Giulio Jacucci, Aki Reijonen and Samuel Kaski. Lost in Publications? How to Find Your Way in 50 Million Scientific Documents. Refereed extended abstract (5 pages) in the NIPS 2013 workshop on Big Learning: Advances in Algorithms and Data Management, 2013. (pdf extended abstract)

  7. Jaakko Peltonen. Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization. Abstract in Daniel E. Keim, Fabrice Rossi, Thomas Seidl, Michel Verleysen, and Stefan Wrobel, editors, Information Visualization, Visual Data Mining and Machine Learning, Dagstuhl Reports, 2(2): 58-83, 2012. (Online report)

  8. Jaakko Peltonen and Samuel Kaski. Generative modeling for maximizing precision and recall in information visualization. Technical Report TKK-ICS-R38, Aalto University School of Science and Technology, Department of Information and Computer Science, Espoo, Finland, November 2010.

  9. Jaakko Peltonen, Yusuf Yaslan, and Samuel Kaski. Variational Bayes Learning from Relevant Tasks Only. Refereed extended abstract (4 pages), in the NIPS 2008 Learning from Multiple Sources Workshop. (abstract, pdf extended abstract)

  10. Tapani Raiko and Jaakko Peltonen. Application of UCT search to the connection games of Hex, Y, *Star, and Renkula! In proceedings of the Finnish Artificial Intelligence Conference (SteP 2008), Espoo, Finland, August 2008. See also the associated 3D boardgame.

  11. Mikko A. Uusitalo and Jaakko Peltonen. Nanocomputing with machine learning. Poster in Nanotech Northern Europe 2008 (NTNE 2008), 2008.

  12. Merja Oja, Jaakko Peltonen, Jonas Blomberg and Samuel Kaski. Estimating human endogeneous retrovirus activities in various tissues with a hidden Markov mixture model. Poster in Intelligent Systems for Molecular Biology & European Conference on Computational Biology 2007 (ISMB/ECCB 2007), Vienna, Austria, July 21-25, 2007.

  13. Samuel Kaski and Jaakko Peltonen. Learning from Relevant Tasks Only. Technical Report E11, Helsinki University of Technology, Publications in Computer and Information Science, May 2007. (pdf)

  14. Jaakko Peltonen and Samuel Kaski. Learning when only some of the training data are from the same distribution as test data. Poster in the NIPS 2006 workshop on Learning when test and training inputs have different distributions, December 9, Whistler, Canada. (abstract, pdf extended abstract, pdf poster in A0 size)

  15. Jaakko Peltonen, Jacob Goldberger, and Samuel Kaski. Fast Discriminative Component Analysis for Comparing Examples. Refereed extended abstract (5 pages). In NIPS 2006 workshop on Learning to Compare Examples. (abstract, pdf)

  16. Merja Oja, Jaakko Peltonen, and Samuel Kaski. A hidden Markov model for estimating human endogenous retrovirus activities from expressed sequence databases. Poster in the European Conference on Computational Biology (ECCB 2006), Eilat, Israel, January 21-24, 2007. (abstract)

  17. Jaakko Peltonen, Janne Sinkkonen, and Samuel Kaski. Finite Sequential Information Bottleneck (fsIB). Technical Report A74, Helsinki University of Technology, Publications in Computer and Information Science, Espoo, Finland, December 2003.

Theses


  1. Jaakko Peltonen. Data Exploration with Learning Metrics. D.Sc. thesis. Dissertations in Computer and Information Science, Report D7. Espoo, Finland, 2004. Award-winning: Doctoral thesis award of the Pattern Recognition Society of Finland, for the best Finnish doctoral thesis in the field of pattern recognition 2004-2005.

  2. Jaakko Peltonen. Self-organizing maps in learning metrics. Master's Thesis, Helsinki University of Technology, Department of Engineering Physics and Mathematics, 2001. Award-winning: Master's thesis award 2002 for best Finnish Master's thesis in technology, granted by Tekniikan Akateemisten Liitto TEK ry and Tekniska Föreningen i Finland TFiF r.f.

Popular-science publications


  1. Jaakko Peltonen. Itseorganisoituvat kartat oppivissa metriikoissa. Tekniikan Akateemiset 5/2002. (pdf at publisher pages)

Some Useful Resources

PASCAL2 network, Google Scholar, IEEE Xplore, Lecture Notes in Computer Science (Springer), NIPS proceedings, KDnuggets, mldata.org data set repository.