Competitive Learning Algorithms Founded on Adaptivity and Sensitivity Deletion Methods

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概要

• 論文の詳細を見る

• This paper describes two competitive learning algorithms from the viewpoint of deleting mechanisms of weight (reference) vectors. The techniques are termed the adaptivity and sensitivity deletions participated in the criteria of partition error and distortion error, respectively. Experimental results show the effectiveness of the proposed approaches in the average distortion.

• 社団法人電子情報通信学会の論文
• 2000-12-25

著者

• MIYAJIMA Hiromi

  the Faculty of Engineering, Kagoshima University

• MAEDA Michiharu

  the Department of Control & Information Systems Engineering at Kurume National College of Technology

• Maeda Michiharu

  The Department Of Control And Information Systems Engineering Kurume National College Of Technology

• Miyajima Hiromi

  The Faculty Of Engineering Kagoshima University

関連論文

• An Investigation of Fuzzy Model Using AIC
• Destructive Fuzzy Modeling Using Neural Gas Network
• Competitive Learning Algorithms Founded on Adaptivity and Sensitivity Deletion Methods
• Some Characteristics of Higher Order Neural Networks with Decreasing Energy Functions (Special Section on Nonlinear Theory and its Applications)
• An Adaptive Learning and Self-Deleting Neural Network for Vector Quantization
• Adaptation Strength According to Neighborhood Ranking of Self-Organizing Neural Networks(Nonlinear Theory and Its Applications)

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