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Committee Date Time Place Paper Title / Authors Abstract Paper #
IBISML 2017-11-09
13:00		 Tokyo Univ. of Tokyo [Poster Presentation] Empirical Bayesian Tree
Masashi Sekino (SMN) IBISML2017-39		 We propose a new decision tree learning algorithm ``Empirical Bayesian Tree (EBT)'', which models the outputs of a leaf ... [more] IBISML2017-39
pp.31-38
IBISML 2010-11-05
15:30		 Tokyo IIS, Univ. of Tokyo [Poster Presentation] Probabilistic Matrix Factorization based on Features
Masashi Sekino (Sony) IBISML2010-87		 We propose Probabilistic Matrix Factorized Regression (PMFR) which enables Probabilistic Matrix Factorization (PMF) to u... [more] IBISML2010-87
pp.203-210
MBE, NC
(Joint)		 2007-12-22
11:15		 Aichi   Rank Reduction by Cross-Validated Backpropagation
Masashi Sekino, Katsumi Nitta (Tokyo Inst. of Tech.) NC2007-76		  [more] NC2007-76
pp.31-36
NC 2007-05-21
10:25		 Kanagawa Tokyo Inst. Tech.(Suzukakedai Campus) Unbiased Likelihood Backpropagation Learning
Masashi Sekino, Katsumi Nitta (Tokyo Inst. of Tech.) NC2007-1		 The error backpropagation is one of the popular methods for training an artificial neural network.When the error backpro... [more] NC2007-1
pp.1-6
NC 2007-03-14
10:10		 Tokyo Tamagawa University Unbiased Learning for Hierarchical Models
Masashi Sekino, Katsumi Nitta (Tokyo Tech) 		It is known that overfitting occurs when a conventional statistical learning method such as maximum likelihood estimatio... [more] NC2006-136
pp.109-114
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