A hierarchical learning system incorporating with supervised, unsupervised and reinforcement learning

Jinglu Hu*, Takafumi Sasakawa, Kotaro Hirasawa, Huiru Zheng

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

According to Hebb's Cell assembly theory, the brain has the capability of function localization. On the other hand, it is suggested that the brain has three different learning paradigms: supervised, unsupervised and reinforcement learning. Inspired by the above knowledge of brain, we present a hierarchical learning system consisting of three parts: supervised learning (SL) part, unsupervised learning (UL) part and reinforcement learning (RL) part. The SL part is a main part learning input-output mapping; the UL part realizes the function localization of learning system by controlling firing strength of neurons in SL part based on input patterns; the RL part optimizes system performance by adjusting parameters in UL part. Simulation results confirm the effectiveness of the proposed hierarchical learning system.

本文言語English
ホスト出版物のタイトルAdvances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings
出版社Springer Verlag
ページ403-412
ページ数10
PART 1
ISBN(印刷版)9783540723820
DOI
出版ステータスPublished - 2007
イベント4th International Symposium on Neural Networks, ISNN 2007 - Nanjing, China
継続期間: 2007 6 32007 6 7

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
番号PART 1
4491 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference4th International Symposium on Neural Networks, ISNN 2007
国/地域China
CityNanjing
Period07/6/307/6/7

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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