@techreport{oai:ipsj.ixsq.nii.ac.jp:00090427,
 author = {Jun, Kitazono and Kenji, Nagata and Shinichi, Nakajima and Akira, Manda and Satoshi, Eifuku and Ryoi, Tamura and Masato, Okada and Jun, Kitazono and Kenji, Nagata and Shinichi, Nakajima and Akira, Manda and Satoshi, Eifuku and Ryoi, Tamura and Masato, Okada},
 issue = {8},
 month = {Feb},
 note = {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., 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.},
 title = {Exhaustive Search of Feature Subsets for Support Vector Machine Classification},
 year = {2013}
}