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) |
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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) |
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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 |
6 |
Date of Issue |
2011-07-18 (NC) |