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Paper Abstract and Keywords
Presentation 2010-06-14 09:35
[Invited Talk] Advances in Statistical Machine Learning -- An Approach based on Probability Density Ratios --
Masashi Sugiyama (Tokyo Inst. of Tech.) IBISML2010-1
Abstract (in Japanese) (See Japanese page) 
(in English) Recently, we developed a new ML framework that allows us to
systematically avoid density estimation. The key idea is to directly
estimate the ratio of density functions, not densities themselves.
Our framework includes various ML tasks such as importance sampling
(e.g., covariate shift adaptation, transfer learning, multitask
learning), divergence estimation (e.g., two-sample test, outlier
detection, change detection in time-series), mutual information
estimation (e.g., independence test, independent component analysis,
feature selection, sufficient dimension reduction, causal inference),
and conditional probability estimation (e.g., probabilistic
classification, conditional density estimation).

In this talk, I introduce the density ratio framework, review methods
of density ratio estimation, and show various real-world applications
including brain-computer interface, speech recognition, image
recognition, and robot control.
Keyword (in Japanese) (See Japanese page) 
(in English) probability density ratios / importance sampling / divergence estimation / mutual information estimation / conditional probability estimation / / /  
Reference Info. IEICE Tech. Rep., vol. 110, no. 76, IBISML2010-1, pp. 1-1, June 2010.
Paper # IBISML2010-1 
Date of Issue 2010-06-07 (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 IBISML2010-1

Conference Information
Committee IBISML  
Conference Date 2010-06-14 - 2010-06-15 
Place (in Japanese) (See Japanese page) 
Place (in English) Takeda Hall, Univ. Tokyo 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine learning, etc. 
Paper Information
Registration To IBISML 
Conference Code 2010-06-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Advances in Statistical Machine Learning 
Sub Title (in English) An Approach based on Probability Density Ratios 
Keyword(1) probability density ratios  
Keyword(2) importance sampling  
Keyword(3) divergence estimation  
Keyword(4) mutual information estimation  
Keyword(5) conditional probability estimation  
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1st Author's Name Masashi Sugiyama  
1st Author's Affiliation Tokyo Institute of Technology (Tokyo Inst. of Tech.)
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Speaker Author-1 
Date Time 2010-06-14 09:35:00 
Presentation Time 50 minutes 
Registration for IBISML 
Paper # IBISML2010-1 
Volume (vol) vol.110 
Number (no) no.76 
Page p.1 
#Pages 1 
Date of Issue 2010-06-07 (IBISML) 

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