Efficient initialization scheme for the self-organizing feature map algorithm

Mu Chun Su, Ta Kang Liu, Hsiao Te Chang

Research output: Contribution to conferencePaperpeer-review

23 Scopus citations

Abstract

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 of the algorithm, namely, the learning-rate parameter and the neighborhood function. 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. Two data sets are tested to illustrate the performance of the proposed method.

Original languageEnglish
Pages1906-1910
Number of pages5
StatePublished - 1999
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: 10 Jul 199916 Jul 1999

Conference

ConferenceInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA
Period10/07/9916/07/99

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