MISC

査読有り
2005年

Multi-class pattern classification based on a probabilistic model of combining binary classifiers

ARTIFICIAL NEURAL NETWORKS: FORMAL MODELS AND THEIR APPLICATIONS - ICANN 2005, PT 2, PROCEEDINGS
  • N Yukinawa
  • ,
  • S Oba
  • ,
  • K Kato
  • ,
  • S Ishii

3697
開始ページ
337
終了ページ
342
記述言語
英語
掲載種別
出版者・発行元
SPRINGER-VERLAG BERLIN

We propose a novel probabilistic model for constructing a multi-class pattern classifier by weighted aggregation of general binary classifiers including one-versus-the-rest, one-versus-one, and others. Our model has a latent variable that represents class membership probabilities, and it is estimated by fitting it to probability estimate outputs of binary classfiers. We apply our method to classification problems of synthetic datasets and a real world dataset of gene expression profiles. We show that our method achieves comparable performance to conventional voting heuristics.

リンク情報
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000232196000054&DestApp=WOS_CPL
ID情報
  • ISSN : 0302-9743
  • Web of Science ID : WOS:000232196000054

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