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

Jinglu Hu, Takafumi Sasakawa, Kotaro Hirasawa, Huiru Zheng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationAdvances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings
PublisherSpringer Verlag
Pages403-412
Number of pages10
EditionPART 1
ISBN (Print)9783540723820
DOIs
Publication statusPublished - 2007
Event4th International Symposium on Neural Networks, ISNN 2007 - Nanjing, China
Duration: 2007 Jun 32007 Jun 7

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume4491 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Symposium on Neural Networks, ISNN 2007
CountryChina
CityNanjing
Period07/6/307/6/7

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Hu, J., Sasakawa, T., Hirasawa, K., & Zheng, H. (2007). A hierarchical learning system incorporating with supervised, unsupervised and reinforcement learning. In Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings (PART 1 ed., pp. 403-412). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4491 LNCS, No. PART 1). Springer Verlag. https://doi.org/10.1007/978-3-540-72383-7_48