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Paper Abstract and Keywords
Presentation 2018-03-19 14:45
Optimization of Gaussian Kernel Parameters for Kernel Logistic Regression
Kosuke Fukumori, Tomoya Wada, Toshihisa Tanaka (TUAT) EA2017-135 SIP2017-144 SP2017-118
Abstract (in Japanese) (See Japanese page) 
(in English) The kernel logistic regression is a nonlinear classification model that effectively uses kernel methods, which are one of the techniques to construct effective nonlinear systems with a reproducing kernel Hilbert space (RKHS) induced from a positive definite kernel.
Since a performance of the kernel logistic regression with RKHS depends on the kernels to build the model, it is important to select appropriate kernel parameters.
In this paper, we propose a method to optimize the kernel widths at learning for the kernel logistic regression using Gaussian kernels.
In addition to that, the kernel centers are also updated to increase the generalization ability.
For learning of kernel coefficients, we introduce L1-regularization to reduce the number of support vectors.
Numerical experiments support the validity of the proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) Kernel logistic regression / Nonlinear classification / Reproducing kernel Hilbert space / Gaussian kernel / / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 516, SIP2017-144, pp. 185-190, March 2018.
Paper # SIP2017-144 
Date of Issue 2018-03-12 (EA, SIP, SP) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF EA2017-135 SIP2017-144 SP2017-118

Conference Information
Committee SIP EA SP MI  
Conference Date 2018-03-19 - 2018-03-20 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) Speech, Engineering/Electro Acoustics, Signal Processing, and Related Topics [SIP, EA, SP]/ Medical Image Engineering, Analysis, Recognition, etc. [MI] 
Paper Information
Registration To SIP 
Conference Code 2018-03-SIP-EA-SP-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Optimization of Gaussian Kernel Parameters for Kernel Logistic Regression 
Sub Title (in English)  
Keyword(1) Kernel logistic regression  
Keyword(2) Nonlinear classification  
Keyword(3) Reproducing kernel Hilbert space  
Keyword(4) Gaussian kernel  
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1st Author's Name Kosuke Fukumori  
1st Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
2nd Author's Name Tomoya Wada  
2nd Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
3rd Author's Name Toshihisa Tanaka  
3rd Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
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Speaker Author-1 
Date Time 2018-03-19 14:45:00 
Presentation Time 25 minutes 
Registration for SIP 
Paper # EA2017-135, SIP2017-144, SP2017-118 
Volume (vol) vol.117 
Number (no) no.515(EA), no.516(SIP), no.517(SP) 
Page pp.185-190 
#Pages 6 
Date of Issue 2018-03-12 (EA, SIP, SP) 

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