Discriminant method for severity of glandular tumor by support vector machine

Ayako Suzuki, Toshiyuki Tanaka

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

In this study, glandular tumor images are classified automatically by the support vector machine (SVM) in order to make up for a fault of discriminant analysis, Mahalanobis' generalized distance which was used in recent studies. The fault of Mahalanobis' generalized distance is the problem, that is to say, the Curse of Dimensionality. To avoid this problem, we used the support vector machine (SVM) as the discriminant analysis, used the prostate images as glandular tumor images ,and examined the effectiveness of this system.

本文言語English
ホスト出版物のタイトルProceedings of SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology
ページ3101-3104
ページ数4
DOI
出版ステータスPublished - 2008
イベントSICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology - Tokyo, Japan
継続期間: 2008 8月 202008 8月 22

出版物シリーズ

名前Proceedings of the SICE Annual Conference

Other

OtherSICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology
国/地域Japan
CityTokyo
Period08/8/2008/8/22

ASJC Scopus subject areas

  • 制御およびシステム工学
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学

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