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) |
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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) |
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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) |
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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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