Unsupervised Learning Algorithms for Multimodal Pattern Classifiers

Hiroyuki Matsunaga, Kiichi Urahama

研究成果: Contribution to journalArticle査読

抄録

Nearest neighbor pattern recognition is represented in terms of robust estimation, and a classification method using multimodal data fusion based on the Bayes rule is proposed. The proposed method is proved to be a kind of fuzzy voting. Unsupervised learning of classes' representative points using the EM algorithm is introduced. The basic properties of the proposed multimodal classifier are examined using simple data, and a qualitative explanation of the McGurk effect is offered. Experimental results on segmentation of multiple images are presented as an example of application.

本文言語英語
ページ(範囲)51-60
ページ数10
ジャーナルSystems and Computers in Japan
30
8
DOI
出版ステータス出版済み - 1 1 1999

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Information Systems
  • Hardware and Architecture
  • Computational Theory and Mathematics

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