Feature selection by genetic algorithm that adopts the principle of margin-maximization and its application to character recognition

Akira Suzuki, Satoshi Shimada, Yoshimasa Kimura, Shunichi Yonemura, Masashi Morimoto

研究成果: Article査読

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.

本文言語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
41
2
出版ステータスPublished - 2012
外部発表はい

ASJC Scopus subject areas

  • コンピュータ サイエンス(その他)
  • 電子工学および電気工学

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