Comparison of Classifiers in Small Training Sample Size Situations for Pattern Recognition

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概要

• 論文の詳細を見る

• The main problem in statistical pattern recognition is to design a classifier. Many researchers point out that a finite number of training samples causes the practical difficulties and constraints in designing a classifier. However, very little is known about the performance of a classifier in small training sample size situations. In this paper, we compare the classification performance of the well-known classifiers (k-NN, Parzen, Fisher's linear, Quadratic, Modified quadratic, Euclidean distance classifiers) when the number of training samples is small.

• 一般社団法人電子情報通信学会の論文
• 1994-03-25

著者

• Hamamoto Yoshihiko

  Faculty Of Engineering Yamaguchi University

• Tomita S

  Shobi Univ. Saitama Jpn

• Tomita Shingo

  Faculty Of Engineering Yamaguchi University Ube 755

• Uchimura Shunji

  Oshima National College of Maritime Technology

関連論文

• On two inference methods for database systems
• Comparison of Classifiers in Small Training Sample Size Situations for Pattern Recognition

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