Self-Organizing Neural Grove and Its Parallel and Distributed Performance

Authors:
Hirotaka Inoue
Affiliations:
Department of Electrical Engineering and Information Science, Kure National College of Technology, Hiroshima, Japan 737-8506
Venue:
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Year:
2009

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Abstract

In this paper, we present the improving capability of accuracy and the parallel efficiency of self-organizing neural groves (SONGs) for classification on a MIMD parallel computer. Self-generating neural networks (SGNNs) are originally proposed on adopting to classification or clustering by automatically constructing self-generating neural tree (SGNT) from given training data. The SONG is composed of plural SGNTs each of which is independently generated by shuffling the order of the given training data, and the output of the SONG is voted all outputs of the SGNTs. We allocate each of SGNTs to each of processors in the MIMD parallel computer. Experimental results show that the more the number of processors increases, the more the classification accuracy increases for all problems.