XCSR Learning from Compressed Data Acquired by Deep Neural Network | Zendy

K Matsumoto | Zendy; Takato Tatsumi | Zendy; Hiroyuki Satō | Zendy; Tim Kovacs | Zendy; Keiki Takadama | Zendy

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XCSR Learning from Compressed Data Acquired by Deep Neural Network

Author(s) -

K Matsumoto,

Takato Tatsumi,

Hiroyuki Satō,

Tim Kovacs,

Keiki Takadama

Publication year - 2017

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2017.p0856

Subject(s) - correctness , computer science , artificial neural network , artificial intelligence , deep learning , classifier (uml) , machine learning , benchmark (surveying) , learning classifier system , reinforcement learning , time delay neural network , algorithm , geodesy , geography

The correctness rate of classification of neural networks is improved by deep learning, which is machine learning of neural networks, and its accuracy is higher than the human brain in some fields. This paper proposes the hybrid system of the neural network and the Learning Classifier System (LCS). LCS is evolutionary rule-based machine learning using reinforcement learning. To increase the correctness rate of classification, we combine the neural network and the LCS. This paper conducted benchmark experiments to verify the proposed system. The experiment revealed that: 1) the correctness rate of classification of the proposed system is higher than the conventional LCS (XCSR) and normal neural network; and 2) the covering mechanism of XCSR raises the correctness rate of proposed system.

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