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- SUGIYAMA Masashi
- Tokyo Institute of Technology PRESTO, Japan Science and Technology Agency
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- TAKEUCHI Ichiro
- Nagoya Institute of Technology
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- SUZUKI Taiji
- The University of Tokyo
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- KANAMORI Takafumi
- Nagoya University
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- HACHIYA Hirotaka
- Tokyo Institute of Technology
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- OKANOHARA Daisuke
- The University of Tokyo
この論文をさがす
抄録
Estimating the conditional mean of an input-output relation is the goal of regression. However, regression analysis is not sufficiently informative if the conditional distribution has multi-modality, is highly asymmetric, or contains heteroscedastic noise. In such scenarios, estimating the conditional distribution itself would be more useful. In this paper, we propose a novel method of conditional density estimation that is suitable for multi-dimensional continuous variables. The basic idea of the proposed method is to express the conditional density in terms of the density ratio and the ratio is directly estimated without going through density estimation. Experiments using benchmark and robot transition datasets illustrate the usefulness of the proposed approach.
収録刊行物
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- IEICE Transactions on Information and Systems
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IEICE Transactions on Information and Systems E93-D (3), 583-594, 2010
一般社団法人 電子情報通信学会
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詳細情報 詳細情報について
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- CRID
- 1390001204379401984
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- NII論文ID
- 10026814428
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- NII書誌ID
- AA10826272
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- ISSN
- 17451361
- 09168532
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可