A Density-ratio Framework for Statistical Data Processing

• Item type: Trans(1)
• 公開日: 2009-09-24
• タイトル: A Density-ratio Framework for Statistical Data Processing
• タイトル / 言語: en
• タイトル: A Density-ratio Framework for Statistical Data Processing
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: Research Paper
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Tokyo Institute of Technology
• 著者所属: Nagoya University
• 著者所属: The University of Tokyo
• 著者所属: IBM Research
• 著者所属: Ochanomizu University
• 著者所属: Nagoya Institute of Technology
• 著者所属: Peking University, Beijing
• 著者所属(英): en
• 著者所属(英): Tokyo Institute of Technology
• 著者所属(英): en
• 著者所属(英): Nagoya University
• 著者所属(英): en
• 著者所属(英): The University of Tokyo
• 著者所属(英): en
• 著者所属(英): IBM Research
• 著者所属(英): en
• 著者所属(英): Ochanomizu University
• 著者所属(英): en
• 著者所属(英): Nagoya Institute of Technology
• 著者所属(英): en
• 著者所属(英): Peking University, Beijing
• 著者名: Masashi, Sugiyama; Takafumi, Kanamori; Taiji, Suzuki; Shohei, Hido; Jun, Sese; Ichiro, Takeuchi; Liwei, Wang
• 著者名(英): Masashi, Sugiyama; Takafumi, Kanamori; Taiji, Suzuki; Shohei, Hido; Jun, Sese; Ichiro, Takeuchi; Liwei, Wang
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In statistical pattern recognition, it is important to avoid density estimation since density estimation is often more difficult than pattern recognition itself. Following this idea―known as Vapnik's principle, a statistical data processing framework that employs the ratio of two probability density functions has been developed recently and is gathering a lot of attention in the machine learning and data mining communities. The purpose of this paper is to introduce to the computer vision community recent advances in density ratio estimation methods and their usage in various statistical data processing tasks such as nonstationarity adaptation, outlier detection, feature selection, and independent component analysis.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In statistical pattern recognition, it is important to avoid density estimation since density estimation is often more difficult than pattern recognition itself. Following this idea―known as Vapnik's principle, a statistical data processing framework that employs the ratio of two probability density functions has been developed recently and is gathering a lot of attention in the machine learning and data mining communities. The purpose of this paper is to introduce to the computer vision community recent advances in density ratio estimation methods and their usage in various statistical data processing tasks such as nonstationarity adaptation, outlier detection, feature selection, and independent component analysis.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA12394973
• 書誌情報: IPSJ Transactions on Computer Vision and Applications（CVA）; 巻 1, p. 183-208, 発行日 2009-09-24
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-6695
• 出版者 / 言語: ja
• 出版者: 情報処理学会