Presentation 1996/5/17
Feature Selection for Acquiring Internal Structure of Recognition System based on Matchability
Hiroshi Yamakawa,
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Abstract(in English) For effective recognition, a recognition structure that controls the information flow among the specialized processing modules should reflect the implicit correlation structure of the environmental input. Autonomous construction of a recognition structure will lead to extensive improve in the flexibility of the adaptive recognition system. For this purpose we propose a matchability-oriented feature selection that can collect highly correlated features at each local module. Matchability is a concept derived from the recognition functions of an adaptive intelligent agent (useful for action generation) and corresponds to the probability of input data items matching stored data items in the recognition system. We check this algorithm in simple artificial environment.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Learning internal representation / Pattern recognition / Neural networks / Multi-agent system / Unsupervised learning / Case based reasoning
Paper # PRMU96-12
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Conference Information
Committee PRMU
Conference Date 1996/5/17(1days)
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Paper Information
Registration To Pattern Recognition and Media Understanding (PRMU)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Feature Selection for Acquiring Internal Structure of Recognition System based on Matchability
Sub Title (in English)
Keyword(1) Learning internal representation
Keyword(2) Pattern recognition
Keyword(3) Neural networks
Keyword(4) Multi-agent system
Keyword(5) Unsupervised learning
Keyword(6) Case based reasoning
1st Author's Name Hiroshi Yamakawa
1st Author's Affiliation Tsukuba Research Center, Real World Computing Partnership()
Date 1996/5/17
Paper # PRMU96-12
Volume (vol) vol.96
Number (no) 41
Page pp.pp.-
#Pages 8
Date of Issue