상세정보
학술대회
Hyperbolic Quotient Feature Map for Competitive Learning Neural Networks
- 저자
- 석진욱, 조성원, 김재민
- 발행일
- 200606
- 출처
- International Symposium on Neural Networks (ISNN) 2006 (LNCS 3971), v.3971, pp.456-463
- 협약과제
- 06MW1100, 임베디드 SW 기반 Smar Town 솔루션 기술 개발, 마평수
- 초록
- 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 제안 키워드
- Competitive Learning, Feature Map, Learning methods, Neural network learning, Pattern recognition, Performance measures, Remote sensing(RS), Remote sensing data, Weight vector, learning algorithms, quotient space