Presentation | 2010-06-14 Advances in Statistical Machine Learning : An Approach based on Probability Density Ratios Masashi SUGIYAMA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(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 eduction, 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) |
Keyword(in English) | probability density ratios / importance sampling / divergence estimation / mutual information estimation / conditional probability estimation |
Paper # | IBISML2010-1 |
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Conference Information | |
Committee | IBISML |
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Conference Date | 2010/6/7(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Information-Based Induction Sciences and Machine Learning (IBISML) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Advances in Statistical Machine Learning : An Approach based on Probability Density Ratios |
Sub Title (in English) | |
Keyword(1) | probability density ratios |
Keyword(2) | importance sampling |
Keyword(3) | divergence estimation |
Keyword(4) | mutual information estimation |
Keyword(5) | conditional probability estimation |
1st Author's Name | Masashi SUGIYAMA |
1st Author's Affiliation | Department of Computer Science, Tokyo Institute of Technology() |
Date | 2010-06-14 |
Paper # | IBISML2010-1 |
Volume (vol) | vol.110 |
Number (no) | 76 |
Page | pp.pp.- |
#Pages | 1 |
Date of Issue |