Incremental learning using self-organizing neural grove

Authors:
Hirotaka Inoue
Affiliations:
Department of Electrical Engineering and Information Science, Kure National College of Technology, Hiroshima, Japan
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Year:
2010

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AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1

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Abstract

Recently, multiple classifier systems (MCS) have been used for practical applications to improve classification accuracy. Self-generating neural networks (SGNN) are one of the suitable base-classifiers for MCS because of their simple setting and fast learning. However, the computation cost of the MCS increases in proportion to the number of SGNN. We proposed a novel pruning method for efficient classification and we call this model as self-organizing neural grove (SONG). In this paper, we investigate SONG's incremental learning performance.