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Paper Abstract and Keywords
Presentation 2012-06-19 10:30
Learning Non-Linear Classifiers with a Sparsity Upper-Bound via Efficient Model Selection
Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara (Kobe Univ.) IBISML2012-2
Abstract (in Japanese) (See Japanese page) 
(in English) Support Vector Machines, when combined with kernels, achieve
state-of-the-art accuracy on many datasets. However, their use in many
real-world applications is hindered by the fact that their model size is
often too large and their prediction function too expensive to evaluate.
In this paper, to address these issues, we are interested in the problem
of learning non-linear classifiers with a sparsity budget. We first define
an L1-regularized convex objective and show how to optimize it, with-
out budget. Next, we show how our approach can be naturally extended
to incorporate a budget by using constrained model selection. Experi-
ments show that, compared to SVMs, our approach leads to much more
parsimonious models with comparable or better accuracy.
Keyword (in Japanese) (See Japanese page) 
(in English) non-linear classification / sparse methods / / / / / /  
Reference Info. IEICE Tech. Rep., vol. 112, no. 83, IBISML2012-2, pp. 9-14, June 2012.
Paper # IBISML2012-2 
Date of Issue 2012-06-12 (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)
Notes on Review This article is a technical report without peer review, and its polished version will be published elsewhere.
Download PDF IBISML2012-2

Conference Information
Committee IBISML  
Conference Date 2012-06-19 - 2012-06-20 
Place (in Japanese) (See Japanese page) 
Place (in English) Campus plaza Kyoto 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General topics on machine learning and its application 
Paper Information
Registration To IBISML 
Conference Code 2012-06-IBISML 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Learning Non-Linear Classifiers with a Sparsity Upper-Bound via Efficient Model Selection 
Sub Title (in English)  
Keyword(1) non-linear classification  
Keyword(2) sparse methods  
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1st Author's Name Mathieu Blondel  
1st Author's Affiliation Kobe University (Kobe Univ.)
2nd Author's Name Kazuhiro Seki  
2nd Author's Affiliation Kobe University (Kobe Univ.)
3rd Author's Name Kuniaki Uehara  
3rd Author's Affiliation Kobe University (Kobe Univ.)
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Speaker Author-1 
Date Time 2012-06-19 10:30:00 
Presentation Time 30 minutes 
Registration for IBISML 
Paper # IBISML2012-2 
Volume (vol) vol.112 
Number (no) no.83 
Page pp.9-14 
#Pages 6 
Date of Issue 2012-06-12 (IBISML) 

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