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Paper Abstract and Keywords
Presentation 2016-09-06 11:15
Hyper-parameter Optimization with Derivative-free Method
Yoshihiko Ozaki, Masaki Yano (Univ. Tsukuba/AIST), Masaki Onishi (AIST), Takahito Kuno (Univ. Tsukuba) PRMU2016-84 IBISML2016-39
Abstract (in Japanese) (See Japanese page) 
(in English) In machine learning methods, an appropriate hyper-parameter tuning is really important for classifiers to perform its best.
However, in general, the relationship between the performance of classifiers and their hyper-parameters is an unknown function.
So the features with the objective function such as gradient are not available to optimize hyper-parameters.
In this paper, we apply derivative-free optimization methods without the gradient information of the objective function in the hyper-parameter tuning of classifiers and evaluated their performance by computational experiments.
Keyword (in Japanese) (See Japanese page) 
(in English) Derivative-Free Optimization / Machine Learning / Hyper-parameter Optimization / Coordinate-Search Method / Nelder-Mead Method / Support Vector Machine / Convolutional Neural Network /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 208, PRMU2016-84, pp. 227-232, Sept. 2016.
Paper # PRMU2016-84 
Date of Issue 2016-08-29 (PRMU, IBISML) 
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 PRMU2016-84 IBISML2016-39

Conference Information
Committee PRMU IPSJ-CVIM IBISML  
Conference Date 2016-09-05 - 2016-09-06 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2016-09-PRMU-CVIM-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Hyper-parameter Optimization with Derivative-free Method 
Sub Title (in English)  
Keyword(1) Derivative-Free Optimization  
Keyword(2) Machine Learning  
Keyword(3) Hyper-parameter Optimization  
Keyword(4) Coordinate-Search Method  
Keyword(5) Nelder-Mead Method  
Keyword(6) Support Vector Machine  
Keyword(7) Convolutional Neural Network  
Keyword(8)  
1st Author's Name Yoshihiko Ozaki  
1st Author's Affiliation University of Tsukuba/National Institute of Advanced Industrial Science and Technology (Univ. Tsukuba/AIST)
2nd Author's Name Masaki Yano  
2nd Author's Affiliation University of Tsukuba/National Institute of Advanced Industrial Science and Technology (Univ. Tsukuba/AIST)
3rd Author's Name Masaki Onishi  
3rd Author's Affiliation National Institute of Advanced Industrial Science and Technology (AIST)
4th Author's Name Takahito Kuno  
4th Author's Affiliation University of Tsukuba (Univ. Tsukuba)
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Speaker Author-1 
Date Time 2016-09-06 11:15:00 
Presentation Time 30 minutes 
Registration for PRMU 
Paper # PRMU2016-84, IBISML2016-39 
Volume (vol) vol.116 
Number (no) no.208(PRMU), no.209(IBISML) 
Page pp.227-232 
#Pages 6 
Date of Issue 2016-08-29 (PRMU, IBISML) 

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