RBF 出力素子を用いたモジュール型ニューラルネット A Modular-Type Neural Network with RBF Output Units

この論文にアクセスする

この論文をさがす

著者

    • 石原 聖司 ISHIHARA Seiji
    • 法政大学工学部経営工学科 Department of Industrial and Systems Engineering, Faculty of Engineering, Hosei University
    • 永野 俊 NAGANO Takashi
    • 法政大学工学部経営工学科 Department of Industrial and Systems Engineering, Faculty of Engineering, Hosei University

抄録

Recently, modular networks have been used to try to solve efficiently multiclass classification problems. However, the rejection rate on patterns of unlearned classes is usually very low. Moreover, when new classes are later added, old modules in the usual modular network need to be re-trained. A modular network proposed in this paper has RBF output units and an algorithm for incremental learning that improve these points. The results of computer simulations showed that the model achieved higher rejection rates on patterns of unlearned classes than the usual modular networks.

収録刊行物

  • 日本神経回路学会誌 = The Brain & neural networks

    日本神経回路学会誌 = The Brain & neural networks 6(4), 203-217, 1999-12-05

    Japanese Neural Network Society

参考文献:  13件中 1-13件 を表示

被引用文献:  2件中 1-2件 を表示

各種コード

  • NII論文ID(NAID)
    10010424371
  • NII書誌ID(NCID)
    AA11658570
  • 本文言語コード
    JPN
  • 資料種別
    ART
  • ISSN
    1340766X
  • データ提供元
    CJP書誌  CJP引用  J-STAGE 
ページトップへ