Feature Selection for Acquiring Internal Structure of Recognition System based on Matchability

• YAMAKAWA Hiroshi

  Tsukuba Research Center, Real World Computing Partnership

Bibliographic Information

Other Title

• 認識システムの内部構造獲得のための Matchability 指向の特徴選択

Abstract

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.

Journal

• Technical report of IEICE. PRMU

  Technical report of IEICE. PRMU 96 (41), 1-8, 1996-05-17

  The Institute of Electronics, Information and Communication Engineers

Keywords

• Learning internal representation
• Pattern recognition
• Neural networks
• Multi-agent system
• Unsupervised learning
• Case based reasoning

Details

• CRID: 1570572702486580864
• NII Article ID: 110003274516
• NII Book ID: AN10541106
• Text Lang: ja
• Data Source:
  • CiNii Articles