Exhaustive Search of Feature Subsets for Support Vector Machine Classification

• Item type: SIG Technical Reports(1)
• 公開日: 2013-02-20
• タイトル: Exhaustive Search of Feature Subsets for Support Vector Machine Classification
• タイトル / 言語: en
• タイトル: Exhaustive Search of Feature Subsets for Support Vector Machine Classification
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Graduate School of Frontier Sciences, The University of Tokyo
• 著者所属: Graduate School of Frontier Sciences, The University of Tokyo
• 著者所属: Nikon Corporation
• 著者所属: Graduate School of Frontier Sciences, The University of Tokyo
• 著者所属: Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama
• 著者所属: Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama
• 著者所属: Graduate School of Frontier Sciences, The University of Tokyo／RIKEN Brain Science Institute
• 著者所属(英): en
• 著者所属(英): Graduate School of Frontier Sciences, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Graduate School of Frontier Sciences, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Nikon Corporation
• 著者所属(英): en
• 著者所属(英): Graduate School of Frontier Sciences, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama
• 著者所属(英): en
• 著者所属(英): Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama
• 著者所属(英): en
• 著者所属(英): Graduate School of Frontier Sciences, The University of Tokyo / RIKEN Brain Science Institute
• 著者名: Jun, Kitazono; Kenji, Nagata; Shinichi, Nakajima; Akira, Manda; Satoshi, Eifuku; Ryoi, Tamura; Masato, Okada
• 著者名(英): Jun, Kitazono; Kenji, Nagata; Shinichi, Nakajima; Akira, Manda; Satoshi, Eifuku; Ryoi, Tamura; Masato, Okada
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Feature selection in machine learning is an important process for improving the generalization capability and interpretability of learned models through the selection of a relevant feature subset. In the last two decades, a number of feature selection methods, such as L1 regularization and automatic relevance determination have been intensively developed and used in a wide range of areas. We can select a relevant subset of features, by using these feature selection methods. In this study, we apply an exhaustive search, instead of these methods, to the neural data recorded in the area of brain involved in face recognition. We evaluate how accurately every subset of recorded neurons can discriminate faces, by using SVM classifiers and cross validation. We show that there are a number of highly accurate neuron subsets. All of these results demonstrate that we should not select only one feature subset but exhaustively evaluate every feature subset.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Feature selection in machine learning is an important process for improving the generalization capability and interpretability of learned models through the selection of a relevant feature subset. In the last two decades, a number of feature selection methods, such as L1 regularization and automatic relevance determination have been intensively developed and used in a wide range of areas. We can select a relevant subset of features, by using these feature selection methods. In this study, we apply an exhaustive search, instead of these methods, to the neural data recorded in the area of brain involved in face recognition. We evaluate how accurately every subset of recorded neurons can discriminate faces, by using SVM classifiers and cross validation. We show that there are a number of highly accurate neuron subsets. All of these results demonstrate that we should not select only one feature subset but exhaustively evaluate every feature subset.
• 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: AN10505667
• 書誌情報: 研究報告数理モデル化と問題解決（MPS）; 巻 2013-MPS-92, 号 8, p. 1-6, 発行日 2013-02-20
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会