抄録
This paper proposes a feature selection method that improves the recognition rate significantly for not only training samples but also unknown samples by using the principle of margin-maximization in the support vector machine (SVM). SVM is well-known as a recognition method that can discriminate unknown samples with high precision, so feature selection with high recognition rates for unknown samples can be expected by adopting the principle of margin-maximization, the technical basis of SVM. We perform experiments on five sets of hand-written Kanji character patterns. Each set consists of patterns in two similar categories. The results show that the proposed method improves the recognition rate significantly for not only training samples but also the unknown samples as expected.
| 元の言語 | English |
|---|---|
| ページ（範囲） | 131-139 |
| ページ数 | 9 |
| ジャーナル | Journal of the Institute of Image Electronics Engineers of Japan |
| 巻 | 41 |
| 発行部数 | 2 |
| 出版物ステータス | Published - 2012 |
| 外部発表 | Yes |
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ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Electrical and Electronic Engineering
これを引用
Feature selection by genetic algorithm that adopts the principle of margin-maximization and its application to character recognition. / Suzuki, Akira; Shimada, Satoshi; Kimura, Yoshimasa; Yonemura, Shunichi; Morimoto, Masashi.
：: Journal of the Institute of Image Electronics Engineers of Japan, 巻 41, 番号 2, 2012, p. 131-139.研究成果: Article
}
TY - JOUR
T1 - Feature selection by genetic algorithm that adopts the principle of margin-maximization and its application to character recognition
AU - Suzuki, Akira
AU - Shimada, Satoshi
AU - Kimura, Yoshimasa
AU - Yonemura, Shunichi
AU - Morimoto, Masashi
PY - 2012
Y1 - 2012
N2 - This paper proposes a feature selection method that improves the recognition rate significantly for not only training samples but also unknown samples by using the principle of margin-maximization in the support vector machine (SVM). SVM is well-known as a recognition method that can discriminate unknown samples with high precision, so feature selection with high recognition rates for unknown samples can be expected by adopting the principle of margin-maximization, the technical basis of SVM. We perform experiments on five sets of hand-written Kanji character patterns. Each set consists of patterns in two similar categories. The results show that the proposed method improves the recognition rate significantly for not only training samples but also the unknown samples as expected.
AB - This paper proposes a feature selection method that improves the recognition rate significantly for not only training samples but also unknown samples by using the principle of margin-maximization in the support vector machine (SVM). SVM is well-known as a recognition method that can discriminate unknown samples with high precision, so feature selection with high recognition rates for unknown samples can be expected by adopting the principle of margin-maximization, the technical basis of SVM. We perform experiments on five sets of hand-written Kanji character patterns. Each set consists of patterns in two similar categories. The results show that the proposed method improves the recognition rate significantly for not only training samples but also the unknown samples as expected.
KW - Character recognition
KW - Feature selection
KW - Genetic algorithms
KW - Margin maximization
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=84902143516&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84902143516&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:84902143516
VL - 41
SP - 131
EP - 139
JO - Journal of the Institute of Image Electronics Engineers of Japan
JF - Journal of the Institute of Image Electronics Engineers of Japan
SN - 0285-9831
IS - 2
ER -