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Committee Date Time Place Paper Title / Authors Abstract Paper #
NLP 2016-05-26
14:50		 Kochi Kochi Univ. Decreasing Gradient of Sigmoid Functions in Back Propagation of Feed-Forward Neural Netowork
Shinaburo Kittaka, Yoko Uwate, Nishio Yoshihumi (Tokushima Univ.) NLP2016-5		 Our study is about the learning method of feed-forward neural network. Generally, the method of improvingitslearningisfo... [more] NLP2016-5
pp.25-28
NC 2007-03-14
10:40		 Tokyo Tamagawa University Optimization of the support vector machine's kernels using a feedforward neural network
Shin'ichi Tamura (DENSO CORP.) 		A novel approach to determining the optimal kernel parameters of Support Vector Machines is proposed. Based on the fact ... [more] NC2006-137
pp.115-120
NC 2006-03-16
10:20		 Tokyo Tamagawa University On the Realization of a Large Margin Non-linear Classifier Using Feed-forward Neural Networks
Shin'ichi Tamura, Makoto Sakai (DENSO) 		It is well known that a feedforward neural network with infinitely many hidden yunits has the capability of realizing an... [more] NC2005-139
pp.85-89
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