Paper Abstract and Keywords |
Presentation |
2014-01-23 10:00
Minimum Classification Error Training with Automatic Control of Loss Smoothness Hideaki Tanaka (Doshisha Univ.), Hideyuki Watanabe (NICT), Shigeru Katagiri, Miho Ohsaki (Doshisha Univ.), Shigeki Matsuda, Chiori Hori (NICT) PRMU2013-92 MVE2013-33 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
The Minimum Classification Error (MCE) training has been successfully applied to various types of classifiers. However, its training power was casted only to the optimization of class boundaries in an original sample space. There is the possibility of applying the MCE training to some other features or feature space, which may lead to more accurate classification. Motivated by this, we introduce in this paper a new MCE training method, called Kernel Minimum Classification Error training. This new method is formalized by applying an MCE training associated with auxiliary-function-based optimization to a classifier that has linear discriminant functions and kernel feature projection. The formalization of the method is characterized by the use of cross-entropy loss as an auxiliary function. We also show basic performances of the method through several evaluation experiments. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Minimum classification error training / Kernel method / Auxiliary function method / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 113, no. 402, PRMU2013-92, pp. 7-12, Jan. 2014. |
Paper # |
PRMU2013-92 |
Date of Issue |
2014-01-16 (PRMU, MVE) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
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PRMU2013-92 MVE2013-33 |
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