Randomized and Dimension Reduced Radial Basis Features for Support Vector Machine
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- Hidaka Akinori
- School of Science and Engineering, Tokyo Denki University
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- Kurita Takio
- Department of Information Engineering, Hiroshima University
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説明
Generally, the dimension of the Kernel matrices of the kernel Support Vector Machines (SVM) increases as the number of training samples increases. However high dimensional features often bring redundant computation and decline of the generalization ability. Also kernel functions have several hyper-parameters which are fixed to the same values for all training samples. By considering the kernel matrix of Radial Basis Function (RBF) as a new high dimensional nonlinear feature for SVM, there is no limitation of which those hyper-parameters have to be a single fixed number. In this paper, we develop a nonlinear feature extraction method based on the selection of kernel seeds and the fine tuning of the kernel parameters, called randomized and dimension Reduced Radial Basis Features (RRBF).
収録刊行物
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- システム制御情報学会論文誌
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システム制御情報学会論文誌 29 (1), 1-8, 2016
一般社団法人 システム制御情報学会
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詳細情報 詳細情報について
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- CRID
- 1390001205167978112
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- NII論文ID
- 130005145542
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- NII書誌ID
- AN1013280X
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- ISSN
- 2185811X
- 13425668
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- NDL書誌ID
- 027040059
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
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