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

Takayuki Furuzuki, 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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages403-412
Number of pages10
Volume4491 LNCS
EditionPART 1
Publication statusPublished - 2007
Event4th International Symposium on Neural Networks, ISNN 2007 - Nanjing
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)03029743
ISSN (Electronic)16113349

Other

Other4th International Symposium on Neural Networks, ISNN 2007
CityNanjing
Period07/6/307/6/7

Fingerprint

Hierarchical Systems
Unsupervised learning
Unsupervised Learning
Supervised learning
Reinforcement learning
Supervised Learning
Learning Systems
Reinforcement Learning
Learning systems
Learning
Brain
Neurons
System Performance
Neuron
Paradigm
Optimise
Reinforcement (Psychology)
Output
Cell
Simulation

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Furuzuki, T., Sasakawa, T., Hirasawa, K., & Zheng, H. (2007). A hierarchical learning system incorporating with supervised, unsupervised and reinforcement learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 4491 LNCS, 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).

A hierarchical learning system incorporating with supervised, unsupervised and reinforcement learning. / Furuzuki, Takayuki; Sasakawa, Takafumi; Hirasawa, Kotaro; Zheng, Huiru.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4491 LNCS PART 1. ed. 2007. p. 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).

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

Furuzuki, T, Sasakawa, T, Hirasawa, K & Zheng, H 2007, A hierarchical learning system incorporating with supervised, unsupervised and reinforcement learning. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 edn, vol. 4491 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 4491 LNCS, pp. 403-412, 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, 07/6/3.
Furuzuki T, Sasakawa T, Hirasawa K, Zheng H. A hierarchical learning system incorporating with supervised, unsupervised and reinforcement learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 ed. Vol. 4491 LNCS. 2007. p. 403-412. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).
Furuzuki, Takayuki ; Sasakawa, Takafumi ; Hirasawa, Kotaro ; Zheng, Huiru. / A hierarchical learning system incorporating with supervised, unsupervised and reinforcement learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4491 LNCS PART 1. ed. 2007. pp. 403-412 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).
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