Koji Tsuda

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Citations	16465	6352
h-index	53	35
i10-index	144	99

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Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedVerified email at tuebingen.mpg.de
Klaus-Robert MüllerTU Berlin & Korea University & Google Brain & Max Planck Institute for InformaticsVerified email at tu-berlin.de
Kiyoshi AsaiProfessor of Computational Biology, University of TokyoVerified email at k.u-tokyo.ac.jp
Hisashi KashimaProfessor, Kyoto UniversityVerified email at i.kyoto-u.ac.jp
Sebastian MikacomtravoVerified email at comtravo.com
Jean-Philippe VertOwkin / PSL UniversityVerified email at mines-paristech.fr
Gunnar RätschProfessor, ETH ZürichVerified email at inf.ethz.ch
Ichigaku TakigawaRIKEN / Hokkaido UniversityVerified email at riken.jp
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
Junichiro ShiomiThe University of TokyoVerified email at photon.t.u-tokyo.ac.jp
Yasuo TabeiUnit leader at RIKEN-AIPVerified email at riken.go.jp
Takeaki UnoNational Institute of Informatics, JapanVerified email at nii.jp
Akihiro InokuchiProfessor, Kwansei Gakuin UniversityVerified email at kwansei.ac.jp
Sebastian NowozinDeepMindVerified email at deepmind.com
Tsuyoshi Kato群馬大学Verified email at cs.gunma-u.ac.jp
Takeuchi IchiroProfessor, Nagoya UniversityVerified email at mae.nagoya-u.ac.jp
Kana ShimizuWaseda University, National Institute of Advanced Industrial Science and TechnologyVerified email at waseda.jp
Jun SeseHumanome Lab Inc.Verified email at humanome.jp
Isao TanakaKyoto UniversityVerified email at kyoto-u.ac.jp
Atsuto SekoKyoto UniversityVerified email at cms.mtl.kyoto-u.ac.jp

Koji Tsuda

Professor, GSFS, The University of Tokyo

Verified email at k.u-tokyo.ac.jp - Homepage

Machine Learning Computational Biology


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MIKA S RATSCH G, et al. An introduction to kernel-based learning algorithms KR Muller Neural Network IEEE Transaction 12 (2), 180-201, 2001	4763*	2001
Marginalized kernels between labeled graphs H Kashima, K Tsuda, A Inokuchi Proceedings of the 20th international conference on machine learning (ICML …, 2003	1073	2003
Kernel methods in computational biology B Schölkopf, K Tsuda, JP Vert MIT press, 2004	974	2004
A primer on kernel methods JP Vert, K Tsuda, B Schölkopf Kernel methods in computational biology 47, 35-70, 2004	509	2004
Prediction of low-thermal-conductivity compounds with first-principles anharmonic lattice-dynamics calculations and Bayesian optimization A Seko, A Togo, H Hayashi, K Tsuda, L Chaput, I Tanaka Physical review letters 115 (20), 205901, 2015	392	2015
Bayesian inference and optimal design in the sparse linear model M Seeger, F Steinke, K Tsuda Artificial Intelligence and Statistics, 444-451, 2007	366	2007
Fast protein classification with multiple networks K Tsuda, H Shin, B Schölkopf Bioinformatics 21 (suppl_2), ii59-ii65, 2005	277	2005
Designing nanostructures for phonon transport via Bayesian optimization S Ju, T Shiga, L Feng, Z Hou, K Tsuda, J Shiomi Physical Review X 7 (2), 021024, 2017	272	2017
Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single-and binary-component solids A Seko, T Maekawa, K Tsuda, I Tanaka Physical Review B 89 (5), 054303, 2014	262	2014
Marginalized kernels for biological sequences K Tsuda, T Kin, K Asai Bioinformatics 18 (suppl_1), S268-S275, 2002	260	2002
Discriminative subsequence mining for action classification S Nowozin, G Bakir, K Tsuda 2007 IEEE 11th International Conference on Computer Vision, 1-8, 2007	255	2007
COMBO: An efficient Bayesian optimization library for materials science T Ueno, TD Rhone, Z Hou, T Mizoguchi, K Tsuda Materials discovery 4, 18-21, 2016	254	2016
Matrix exponentiated gradient updates for on-line learning and Bregman projection K Tsuda, G Rätsch, MK Warmuth Journal of Machine Learning Research 6 (Jun), 995-1018, 2005	240	2005
A new discriminative kernel from probabilistic models K Tsuda, M Kawanabe, G Rätsch, S Sonnenburg, KR Müller Advances in Neural Information Processing Systems 14, 2001	208	2001
ChemTS: an efficient python library for de novo molecular generation X Yang, J Zhang, K Yoshizoe, K Terayama, K Tsuda Science and technology of advanced materials 18 (1), 972-976, 2017	196	2017
Link propagation: A fast semi-supervised learning algorithm for link prediction H Kashima, T Kato, Y Yamanishi, M Sugiyama, K Tsuda Proceedings of the 2009 SIAM international conference on data mining, 1100-1111, 2009	179	2009
Kernels for graphs H Kashima, K Tsuda, A Inokuchi Kernel methods in computational biology, 155-170, 2004	174	2004
gBoost: a mathematical programming approach to graph classification and regression H Saigo, S Nowozin, T Kadowaki, T Kudo, K Tsuda Machine Learning 75, 69-89, 2009	149	2009
Learning kernels from biological networks by maximizing entropy K Tsuda, WS Noble Bioinformatics 20 (suppl_1), i326-i333, 2004	145	2004
Weighted substructure mining for image analysis S Nowozin, K Tsuda, T Uno, T Kudo, G BakIr 2007 IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2007	130	2007

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