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Paper Abstract and Keywords
Presentation 2011-07-25 13:45
General Framework for Local Variational Approximation of Bayesian Learning Using Bregman Divergence
Kazuho Watanabe (NAIST), Masato Okada (Univ. of Tokyo), Kazushi Ikeda (NAIST) NC2011-25
Abstract (in Japanese) (See Japanese page) 
(in English) The local variational method is a technique to approximate an intractable posterior distribution in Bayesian learning. This article formulates a general framework for local variational approximation using the Bregman divergence. Based on a geometrical argument in the space of approximating posteriors, we propose an efficient method to evaluate an upper bound of the marginal likelihood. We demonstrate its application to the kernelized logistic regression model and numerically investigate the accuracy of approximation.
Keyword (in Japanese) (See Japanese page) 
(in English) Bayesian Learning / Local Variational Approximation / Kullback Information / Bregman Divergence / Kernelized Logistic Regression / / /  
Reference Info. IEICE Tech. Rep., vol. 111, no. 157, NC2011-25, pp. 25-30, July 2011.
Paper # NC2011-25 
Date of Issue 2011-07-18 (NC) 
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 NC2011-25

Conference Information
Committee NC  
Conference Date 2011-07-25 - 2011-07-26 
Place (in Japanese) (See Japanese page) 
Place (in English) Graduate School of Engineering, Kobe University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Intelligent systems and general 
Paper Information
Registration To NC 
Conference Code 2011-07-NC 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) General Framework for Local Variational Approximation of Bayesian Learning Using Bregman Divergence 
Sub Title (in English)  
Keyword(1) Bayesian Learning  
Keyword(2) Local Variational Approximation  
Keyword(3) Kullback Information  
Keyword(4) Bregman Divergence  
Keyword(5) Kernelized Logistic Regression  
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1st Author's Name Kazuho Watanabe  
1st Author's Affiliation Nara Institute of Science and Technology (NAIST)
2nd Author's Name Masato Okada  
2nd Author's Affiliation The University of Tokyo (Univ. of Tokyo)
3rd Author's Name Kazushi Ikeda  
3rd Author's Affiliation Nara Institute of Science and Technology (NAIST)
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Speaker Author-1 
Date Time 2011-07-25 13:45:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NC2011-25 
Volume (vol) vol.111 
Number (no) no.157 
Page pp.25-30 
#Pages
Date of Issue 2011-07-18 (NC) 


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