Statistical Mechanics of Adaptive Weight Perturbation Learning

この論文にアクセスする

この論文をさがす

著者

抄録

Weight perturbation learning was proposed as a learning rule in which perturbation is added to the variable parameters of learning machines. The generalization performance of weight perturbation learning was analyzed by statistical mechanical methods and was found to have the same asymptotic generalization property as perceptron learning. In this paper we consider the difference between perceptron learning and AdaTron learning, both of which are well-known learning rules. By applying this difference to weight perturbation learning, we propose adaptive weight perturbation learning. The generalization performance of the proposed rule is analyzed by statistical mechanical methods, and it is shown that the proposed learning rule has an outstanding asymptotic property equivalent to that of AdaTron learning.

収録刊行物

  • IEICE transactions on information and systems

    IEICE transactions on information and systems 94(10), 1937-1940, 2011-10-01

    一般社団法人 電子情報通信学会

参考文献:  14件中 1-14件 を表示

各種コード

  • NII論文ID(NAID)
    10030193342
  • NII書誌ID(NCID)
    AA10826272
  • 本文言語コード
    ENG
  • 資料種別
    SHO
  • ISSN
    09168532
  • データ提供元
    CJP書誌  J-STAGE 
ページトップへ