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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
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Committee Date Time Place Paper Title / Authors Abstract Paper #
IBISML 2015-03-06
Kyoto Kyoto University Model selection with approximate validation error guarantee for (L^2_2) regularized convex loss minimization problems
Atsushi Shibagaki, Yoshiki Suzuki, Ichiro Takeuchi (NIT) IBISML2014-96
In this paper we propose a new algorithm that can select an approximately optimal regularization parameter in a class of... [more] IBISML2014-96
IBISML 2014-11-17
Aichi Nagoya Univ. [Poster Presentation] Efficient leave-one-out cross-validation for L2-regularized classifier
Shota Okumura, Yoshiki Suzuki, Kohei Ogawa, Yuki Shinmura, Ichiro Takeuchi (NIT) IBISML2014-44
Leave-one-out cross-validation (LOOCV) is a useful tool
for estimating generalization performances of
various machine ... [more]
IBISML 2013-11-12
Tokyo Tokyo Institute of Technology, Kuramae-Kaikan [Poster Presentation] Safe Sample Screening Rule on Hinge Loss Minimization
Kohei Ogawa, Yamato Kawamoto, Yoshiki Suzuki, Ichiro Takeuchi (Nagoya Inst. of Tech.) IBISML2013-39
In this paper, we propose the algorithm that can speed up computing problems minimizing Hinge loss such as SVMs, via eli... [more] IBISML2013-39
IBISML 2013-11-13
Tokyo Tokyo Institute of Technology, Kuramae-Kaikan [Poster Presentation] Safe Screening Rule for Incmrenetal Learning
Yoshiki Suzuki, Shota Okumura, Kohei Ogawa, Ichiro Takeuchi (Nagoya Inst. of Tech.) IBISML2013-64
Efficient optimization algorithm is required in online learning or other incremental learning scenario since the model m... [more] IBISML2013-64
IBISML 2013-03-05
Aichi Nagoya Institute of Technology Computing pathwise SVMs by Using Non-Support Vector Screening
Kohei Ogawa, Yoshiki Suzuki, Ichiro Takeuchi (Nagoya Inst. of Tech.) IBISML2012-108
In this paper, we claim that some of the non-support vectors (non-SVs) that have no influence on the classifier can be s... [more] IBISML2012-108
IBISML 2012-11-08
Tokyo Bunkyo School Building, Tokyo Campus, Tsukuba Univ. Efficient SVM Bootstrap Computation by Parametric Programming
Yoshiki Suzuki, Kohei Ogawa, Ichiro Takeuchi (Nagoya Inst. of Tech.) IBISML2012-73
In this paper, we study statistical variability estimation of the support vector machine (SVM) by using bootstrap method... [more] IBISML2012-73
 Results 1 - 6 of 6  /   
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