Nonlinear Discriminant Analysis Using Feed-forward Neural Networks : An Application to Diabetes Classification

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概要

• 論文の詳細を見る

• This paper is concerned with the techniques for nonlinear discriminant analysis from the standpoint of comparison with hidden-layer feed-forward neural networks. We discuss learning algorithms by use of maximum likelihood method through Kullback-Leibler measure. Akaike's information criterion provides us the decision as to which of several competing network architectures is "best" for a given problem. We contrast the merits of hidden-layer feed-forward neural networks with those of ordinary discriminat analysis.

• バイオメディカル・ファジィ・システム学会の論文
• 1997-10-16

著者

• Tsujitani Masaaki

  Department of Engineering Informatics, Osaka Electro-Communication University

• Tsujitani Masaaki

  Department Of Engineering Informatics Osaka Electro-communication University

• KOSHIMIZU Takashi

  Graduate School of Engineering, Osaka Electro-Communication University

• Koshimizu Takashi

  Graduate School Of Engineering Osaka Electro-communication University

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• PROPERTY OF THE RC(M) ASSOCIATION MODEL AND A SUMMARY MEASURE OF ASSOCIATION IN THE CONTINGENCY TABLE
• Nonlinear Discriminant Analysis Using Feed-forward Neural Networks : An Application to Diabetes Classification
• TWO-STAGE TESTING TO ESTABLISH NON-INFERIORITY IN THE STRATIFIED 2×2 CONTINGENCY TABLES
• ASSOCIATION MODELS WITH LOCATION AND DISPERSION SCORES FOR THE ANALYSIS OF SINGLY-ORDERED CONTINGENCY TABLES

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