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Committee Date Time Place Paper Title / Authors Abstract Paper #
IBISML 2018-03-06
Fukuoka Nishijin Plaza, Kyushu University Learning rule-base model by Safe Pattern Pruning
Hiroki Kato, Hiroyuki Hanada (Nagoya Inst. of Tech.), Ichiro Takeuchi (Nagoya Inst. of Tech./RIKEN/NIMS) IBISML2017-98
We consider learning the prediction model called ''rule-base model''. Rule-base model is the model which uses ''rules'' ... [more] IBISML2017-98
IBISML 2017-03-07
Tokyo Tokyo Institute of Technology Doubly Accelerated Stochastic Variance Reduced Gradient Method for Regularized Empirical Risk Minimization
Tomoya Murata, Taiji Suzuki (Tokyo Tech) IBISML2016-106
We develop a new stochastic gradient method for solving convex regularized empirical risk minimization problem in mini-b... [more] IBISML2016-106
IBISML 2016-11-17
Kyoto Kyoto Univ. Empirical risk minimization for interval data and its applications to privacy preservations
Hiroyuki Hanada, Toshiyuki Takada, Atsushi Shibagaki (NITech), Jun Sakuma (Univ. of Tsukuba), Ichiro Takeuchi (NITech) IBISML2016-89
In this research, for machine learning tasks, we consider that the values in the training data are given as intervals an... [more] IBISML2016-89
PRMU, IPSJ-CVIM, IBISML [detail] 2016-09-06
Toyama   A proposal on quick sensitivity analysis of empirical risk minimization problems
Hiroyuki Hanada, Atsushi Shibagaki (NITech), Jun Sakuma (Univ. of Tsukuba), Ichiro Takeuchi (NITech) PRMU2016-80 IBISML2016-35
For a training data set consisting of $n$ vectors of $d$ dimensions, we consider obtaining a training result from it by ... [more] PRMU2016-80 IBISML2016-35
PRMU, IBISML, IPSJ-CVIM [detail] 2014-09-01
Ibaraki   Neutralized Empirical Risk Minimization with Covariance-based Neutrality Risk
Kazuto Fukuchi, Jun Sakuma (Univ. of Tsukuba) PRMU2014-48 IBISML2014-29
In order to apply machine learning algorithms to real world problems, it is necessary to ensure that discrimination, unf... [more] PRMU2014-48 IBISML2014-29
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