Mu-Chun Su This email address is being protected from spambots. You need JavaScript enabled to view it.1 , Ta-Kang Liu2 and Hsiao-Te Chang2

1Department of Computer Science and Information Engineering National Central University Chung Li, Taiwan 320, R.O.C.
2Department of Electrical Engineering Tamkang University Tamsui, Taipei, Taiwan 251, R.O.C.


Received: December 19, 2001
Accepted: February 18, 2002
Publication Date: March 1, 2002

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It is often reported in the technique literature that the success of the self-organizing feature map formation is critically dependent on the initial weights and the selection of main parameters (i.e. the learning-rate parameter and the neighborhood set) of the algorithm. They usually have to be counteracted by the trial-and-error method; therefore, often time consuming retraining procedures have to precede before a neighborhood preserving feature amp is obtained. In this paper, we propose an efficient initialization scheme to construct an initial map. We then use the self-organizing feature map algorithm to make small subsequent adjustments so as to improve the accuracy of the initial map. Several data sets are tested to illustrate the performance of the proposed method.

Keywords: Neural Networks, Self-organizing Feature Map, Unsupervised Learning, Kohonen Algorithm


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