Publications

2010
• H. Saigo, M. Hattori, H. Kashima and K. Tsuda. Reaction Graph Kernels Predict EC Numbers of Unknown Enzymatic Reactions in Plant Secondary Metabolism, BMC Bioinformatics (APBC 2010), 2010, to appear.
• Y. Kawahara, K. Nagano, K. Tsuda and J. Bilmes: Submodularity Cuts and Applications, Advances in Neural Information Processing Systems 22, 2010, to appear.
2009
• M. Kayano, I. Takigawa, M. Shiga, K. Tsuda and H. Mamitsuka. Efficiently Finding Genome-wide Three-way Interactions from Transcript- and Genotype-Data, Bioinformatics, 2009. to appear.
• H. Kashima, Y. Yamanishi, T. Kato, M. Sugiyama and K. Tsuda. Simultaneous Inference of Biological Networks of Multiple Species from Genome-wide Data and Evolutionary Information: A Semi-supervised Approach. Bioinformatics, 2009. to appear.
• E. Georgii, S. Dietmann, T. Uno, P. Pagel and K. Tsuda. Enumeration of Condition-Dependent Dense Modules in Protein Interaction Networks. Bioinformatics, 25:933-940, 2009.
• S. Dietmann, E. Georgii, A. Antonov, K. Tsuda and H.W. Mewes. The DICS repository: module-assisted analysis of disease-related gene lists. Bioinformatics, 25:830-831, 2009.
• H. Saigo, S. Nowozin, T. Kadowaki, T. Kudo, and K. Tsuda. gBoost: A mathematical programming approach to graph classification and regression. Machine Learning, 75:69-89, 2009.
• H. Shin, K. Tsuda, and B. Schoelkopf. Protein functional class prediction with a combined graph. Expert Systems with Applications, 36:3284-3292, 2009.
• E. Georgii, K. Tsuda and B. Schoelkopf: Multi-Way Set Enumeration in Real-Valued Tensors, In Proceedings of the KDD 2009 Workshop on Data Mining using Matrices and Tensors (DMMT09), pages 32-41, 2009
• S. Chiappa, H. Saigo and K. Tsuda: A Bayesian Approach to Graph Regression with Relevant Subgraph Selection. In Proceedings of 2009 SIAM International Conference on Data Mining (SDM), pages 295-304, 2009.
• H. Kashima, T. Kato, Y. Yamanishi, M. Sugiyama, K. Tsuda. Link Propagation: A Fast Semisupervised Learning Algorithm for Link Prediction. In Proceedings of 2009 SIAM International Conference on Data Mining (SDM), pages 1099-1110, 2009.
• H. Kashima, S. Oyama, Y. Yamanishi and K. Tsuda. On Pairwise Kernels: An Efficient Alternative and Generalization Analysis. In Proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pages 1030-1037, 2009.
• K. Tsuda. Machine Learning with Quantum Relative Entropy, Journal of Physics: Conference Series, 143, 012021, 2009.
• K. Tsuda. Data mining for biologists. In X.L. Li and S.K. Ng, editors, Biological Data Mining in Protein Interaction Networks. Chapter 2, pages 14-27, IGI Global, 2009.
2008
• S. Nowozin and K. Tsuda. Frequent Subgraph Retrieval in Geometric Graph Databases. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM2008), pages 953-958, 2008
• H. Saigo and K. Tsuda. Iterative Subgraph Mining for Principal Component Analysis. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM2008), pages 1007-1012, 2008
• H. Saigo, N. Kraemer, and K. Tsuda. Partial least squares regression for graph mining. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2008), pages 578-586, 2008.
• K. Tsuda and K. Kurihara. Graph mining with variational Dirichlet process mixture models. In 2008 SIAM Conference on Data Mining, pages 432-442, 2008.
• K. Tanaka and K. Tsuda. A quantum-statistical-mechanical extension of Gaussian mixture model. Journal of Physics: Conference Series, 95, 012023, 2008
2007
• F. Steinke, M. Seeger, and K. Tsuda. Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models. BMC Systems Biology, 1:51, 2007.
• H. Saigo, T. Uno, and K. Tsuda. Mining complex genotypic features for predicting HIV-1 drug resistance. Bioinformatics, 23(18):2455-2462, 2007.
• K. Tsuda. Entire regularization paths for graph data. In Proceedings of the 24th International Conference on Machine Learning, pages 919-926, 2007.
• S. Nowozin, G. Bakir, and K. Tsuda. Discriminative subsequence mining for action classification. In Proceedings of the 11th IEEE International Conference on Computer Vision (ICCV 2007). IEEE Computer Society, 2007.
• S. Nowozin, K. Tsuda, T. Uno, T. Kudo, and G. Bakir. Weighted substructure mining for image analysis. In Proceedings of the 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007). IEEE Computer Society, 2007.
• T. Ide and K. Tsuda. Change-point detection using Krylov subspace learning. In SIAM Conference on Data Mining (SDM), pages 515-520, 2007.
• M. Seeger, F. Steinke, and K. Tsuda. Bayesian inference and optimal design in the sparse linear model. In 11th International Conference on Artificial Intelligence and Statistics (AI & Statistics 2007), pages 444-451, 2007.
2006
• H. Saigo, T. Kadowaki, and K. Tsuda. A linear programming approach for molecular QSAR analysis. In Proceedings of the International Workshop on Mining and Learning with Graphs (MLG), pages 85-96, 2006. Best Paper Award.
• K. Tsuda and T. Kudo. Clustering graphs by weighted substructure mining. In W.W. Cohen and A. Moore, editors, Proceedings of the 23rd International Conference on Machine Learning (ICML), pages 953-960. ACM Press, 2006.
• M. Hamada, K. Tsuda, T. Kudo, T. Kin, and K. Asai. Mining frequent stem patterns from unaligned RNA sequences. Bioinformatics, 22:2480-2487, 2006.
• Y. Tabei, K. Tsuda, T. Kin, and K. Asai. SCARNA: fast and accurate structural alignment of RNA sequences by matching fixed-length stem fragments. Bioinformatics, 22:1723-1729, 2006.
• T. Kato, Y. Murata, K. Miura, K. Asai, P.B. Horton, K. Tsuda, and W. Fujibuchi. Network-based de-noising improves prediction from microarray data. BMC Bioinformatics, 7(Suppl. 1):S4, 2006.
• H. Shin and K. Tsuda. Prediction of Protein Function from Networks. In O. Chapelle, B. Schoelkopf and A. Zien, editors, Semi-supervised Learning, pages 339-352, MIT Press, 2006.
2005
• K. Tsuda. Propagating distributions on a hypergraph by dual information regularization. In L. De Raedt and S. Wrobel, editors, Proceedings of the 22nd International Conference on Machine Learning , pages 921--928. ACM, 2005.
• K. Tsuda, H.J. Shin, and B. Schoelkopf. Fast protein classification with multiple networks. Bioinformatics (ECCB'05), 21(Suppl. 2):ii59--ii65, 2005.
  
• K. Tsuda, G. Raetsch, and M.K. Warmuth. Matrix exponentiated gradient updates for online learning and Bregman projection. Journal of Machine Learning Research, 6:995--1018, 2005.
• K. Tsuda and G. Raetsch. Image reconstruction by linear programming. IEEE Trans. on Image Processing, 14(6):737-744, 2005.
• T. Kato, K. Tsuda, and K. Asai. Selective integration of multiple biological data for supervised network inference. Bioinformatics, 21(10):2488-2495, 2005.
• K. Tsuda, G. Raetsch, and M.K. Warmuth. Matrix exponentiated gradient updates for online learning and Bregman projection. In L.K. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems 17, pages 1425-1432. MIT Press, 2005.

2004
• K. Tsuda, S. Akaho, M. Kawanabe, and K.R. Mueller. Asymptotic properties of the Fisher kernel. Neural Computation, 16(1):115-137, 2004.
• K. Tsuda and W.S. Noble. Learning kernels from biological networks by maximizing entropy. Bioinformatics, 20(Suppl. 1):i326-i333, 2004.
• K. Tsuda, S. Uda, T. Kin, and K. Asai. Minimizing the cross validation error to mix kernel matrices of heterogeneous biological data. Neural Processing Letters, 19:63-72, 2004.
• B. Schoelkopf, K. Tsuda, and J.P. Vert, editors. Kernel Methods in Computational Biology. MIT Press, 2004.
• J.P. Vert, K. Tsuda, and B. Schoelkopf. A primer on kernel methods. In B. Schoelkopf, K. Tsuda, and J.P. Vert, editors, Kernel Methods in Computational Biology, pages 35-70. MIT Press, 2004.
• H. Kashima, K. Tsuda, and A. Inokuchi. Kernels for graphs. In B. Schoelkopf, K. Tsuda, and J.P. Vert, editors, Kernel Methods in Computational Biology, pages 155-170. MIT Press, 2004.
• T. Kin, T. Kato, and K. Tsuda. Protein classification via kernel matrix completion. In B. Schoelkopf, K. Tsuda, and J.P. Vert, editors, Kernel Methods in Computational Biology, pages 261-274. MIT Press, 2004.

• K. Tsuda and G. Raetsch. Image reconstruction by linear programming. In S. Thrun, L. Saul, and B. Schoelkopf, editors, Advances in Neural Information Processing Systems 16, pages 57-64. MIT Press, 2004.
• G.H. Bakir, A. Zien, and K. Tsuda. Learning to find graph pre-images. In C.E. Rasmussen, H.H. Buelthoff, M.A. Giese, and B. Schoelkopf, editors, Pattern Recognition, Proceedings of the 26th DAGM Symposium, volume LNCS 3175, pages 253-261. Springer Verlag, 2004.
• T. Kato, K. Tsuda, K. Tomii, and K. Asai. A new variational framework for rigid-body alignment. In A. Fred, T. Caelli, R.P.W. Duin, A. Campilho, and D. de Ridder, editors, Proceedings of Joint IAPR International Workshops on Syntactical and Structural Pattern Recognition (SSPR 2004) and Statistical Pattern Recognition (SPR 2004), pages 171-179. Springer Verlag, 2004.
• H.J. Shin, K. Tsuda, and B. Schoelkopf. Protein functional class prediction with a combined graph. In Proc. of the Korean Data Mining Conference, pages 200-219, 2004.

2003
• K. Tsuda, S. Akaho, and K. Asai. The em algorithm for kernel matrix completion with auxiliary data. Journal of Machine Learning Research, 4:67-81, May 2003.
• H. Kashima, K. Tsuda, and A. Inokuchi. Marginalized kernels between labeled graphs. In T. Faucett and N. Mishra, editors, Proceedings of the 20th International Conference on Machine Learning, pages 321-328, Menlo Park, CA, AAAI Press, 2003.
• K. Tsuda, M. Kawanabe, and K.-R. Mueller. Clustering with the Fisher score. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, pages 729-736. MIT Press, 2003.

2002
• K. Tsuda, T. Kin, and K. Asai. Marginalized kernels for biological sequences. Bioinformatics, 18(Suppl. 1):S268-S275, 2002.
• K. Tsuda, M. Kawanabe, G. Raetsch, S. Sonnenburg, and K.-R. Mueller. A new discriminative kernel from probabilistic models. Neural Computation, 14(10):2397-2414, 2002.
• K. Tsuda, M. Sugiyama, and K.-R. Mueller. Subspace information criterion for non-quadratic regularizers - model selection for sparse regressors. IEEE Trans. Neural Networks, 13(1):70-80, 2002.
• M. Arita, K. Tsuda, and K. Asai. Modeling splicing sites with pairwise correlations. Bioinformatics, 18(Suppl. 2):S27-S34, 2002.
• T. Kin, K. Tsuda, and K. Asai. Marginalized kernels for RNA sequence data analysis. In R.H. Lathtop, K. Nakai, S. Miyano, T. Takagi, and M. Kanehisa, editors, Genome Informatics 2002, pages 112-122. Universal Academic Press, 2002.
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• K. Tsuda and M. Kawanabe. The leave-one-out kernel. In J.R. Dorronsoro, editor, Artificial Neural Networks - ICANN 2002, LNCS 2415, pages 727-732. Springer Verlag, 2002.
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• K. Tsuda, M. Kawanabe, G. Raetsch, S. Sonnenburg, and K.-R. Mueller. A new discriminative kernel from probabilistic models. In T.G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, pages 977-984. MIT Press, 2002.
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2001
• S. Muraki, T. Nakai, Y. Kita, and K. Tsuda. An attempt for coloring multichannel MR imaging data. IEEE Trans. Visualization and Computer Graphics, 7(3):265-274, 2001.
  
• K.-R. Mueller, S. Mika, G. Raetsch, K. Tsuda, and B. Schoelkopf. An introduction to kernel-based learning algorithms. IEEE Trans. Neural Networks, 12(2):181-201, 2001.
  
• K. Tsuda, G. Raetsch, S. Mika, and K.-R. Mueller. Learning to predict the leave-one-out error of kernel based classifiers. In G. Dorffner, H. Bischof, and K. Hornik, editors, Artifical Neural Networks - ICANN 2001, LNCS 2130, pages 331-338. Springer Verlag, 2001.
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• V. Roth and K. Tsuda. Pairwise coupling for machine recognition of hand-printed Japanese characters. In IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pages 1120-1125, 2001.
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2000
• K. Tsuda and S. Akaho. Large margin classifier via semiparametric inference. In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2000), volume 2, pages 23-29. IEEE CS Press, 2000.
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• K.-R. Mueller, S. Mika, G. Raetsch, K. Tsuda, and B. Schoelkopf. An introduction to kernel-based learning algorithms. In Yu Hen Hu and Jang-Neng Hwang, editors, Handbook of Neural Network Signal Processing. CRC Press, 2000.

1999
• K. Tsuda. Subspace classifier in the Hilbert space. Pattern Recognition Letters, 20:513-519, 1999.
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• K. Tsuda. Optimal hyperplane classifier based on entropy number bound. In ICANN 99: Ninth International Conference on Artifical Neural Networks, pages 419-424. IEE, 1999.
  
• K. Tsuda. Subspace classifier in reproducing kernel hilbert space. In International Joint Conference on Neural Networks (IJCNN'99) Proceedings, 1999.
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• K. Tsuda. Support vector classifier with asymmetric kernel functions. In M. Verleysen, editor, Proceedings of ESANN'99 - European Symposium of Artifical Neural Networks, pages 183-188. D Facto, 1999.
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• K. Tsuda. Optimal hyperplane classifier with adaptive norm. Technical report, ETL Technical report TR-99-9, 1999.
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Before 1998
• H. Yoshiuchi, K. Tsuda, S. Fukushima, and M. Minoh. Pattern recognition method for metric space by four points embedding. In Image and Vision Computing New Zealand (IVCNZ'98), pages 186-191, 1998.
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• K. Tsuda, S. Senda, M. Minoh, and K. Ikeda. Sequential fuzzy cluster extraction and its robustness against noise. Systems and Computers in Japan, 28(6):10-17, 1997.
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• K. Tsuda and M. Minoh. A nonparametric density model for classification in a high dimensional space. In Proc. 4th Int. Conf. Document Analysis and Recognition, pages 1082-1086, 1997.
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• K. Tsuda and M. Minoh. Extracting straight lines by sequential fuzzy clustering. Pattern Recognition Letters, 17:643-649, 1996.
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• T. Taoda, K. Tsuda, and M. Minoh. Generating stereo images from a sequence of monocular images. In Proc. of Int. Conf. Virtual Systems and Multimedia, pages 368-373, 1996.
  
• K. Tsuda, S. Senda, M. Minoh, and K. Ikeda. Clustering OCR-ed texts for browsing document image database. In Proc. 3rd Int. Conf. Document Analysis and Recognition, pages 171-174, 1995.
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