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Conference Paper
Hyperbolic Quotient Feature Map for Competitive Learning Neural Networks
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- Authors
- Jin Wuk Seok, Seong Won Cho, Jae Min Kim
- Issue Date
- 2006-06
- Citation
- International Symposium on Neural Networks (ISNN) 2006 (LNCS 3971), v.3971, pp.456-463
- Publisher
- Springer
- Language
- English
- Type
- Conference Paper
- Abstract
- In this paper, we present a new learning method called hyperbolic quotient feature map for competitive learning neural networks. The previous neural network learning algorithms didn't consider their topological properties, and thus their dynamics were not clearly defined. We show that the weight vectors obtained by competitive learning decompose the input vector space and map it to the quotient space X/R. In addition, we define a quotient function which maps [1, ?닞) ?뒄 Rn to [0, 1) and induce the proposed algorithm from the performance measure with the quotient function. Experimental results for pattern recognition of remote sensing data indicate the superiority of the proposed algorithm in comparison to the conventional competitive learning methods. © Springer-Verlag Berlin Heidelberg 2006.
- KSP Keywords
- Competitive Learning, Feature map, Learning methods, Pattern recognition, Performance measures, Remote Sensing(RS), Remote sensing data, learning algorithm, neural network(NN), neural network learning, quotient space