Incremental learning using self-organizing neural grove

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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. © 2010 Springer-Verlag Berlin Heidelberg.

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Inoue, H. (2010). Incremental learning using self-organizing neural grove. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6354 LNCS, pp. 557–562). https://doi.org/10.1007/978-3-642-15825-4_77

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