Efficient Incremental Learning Using Self-Organizing Neural Grove

Authors:
Hirotaka Inoue;Hiroyuki Narihisa
Affiliations:
Department of Electrical Engineering and Information Science, Kure College of Technology, Hiroshima, Japan 737-8506;Department of Information and Computer Engineering, Okayama University of Science, Okayama, Japan 700-0005
Venue:
Neural Information Processing
Year:
2007

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Abstract

Multiple classifier systems (MCS) have become popular during the last decade. Self-generating neural tree (SGNT) is one of the suitable base-classifiers for MCS because of the simple setting and fast learning. In an earlier paper, we proposed a pruning method for the structure of the SGNT in the MCS to reduce the computational cost and we called this model as self-organizing neural grove (SONG). In this paper, we investigate a performance of incremental learning using SONG for a large scale classification problem. The results show that the SONG can ensure rapid and efficient incremental learning.