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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 저널정보
- 대한전자공학회 ITC-CSCC :International Technical Conference on Circuits Systems, Computers and Communications ITC-CSCC : 2009
- 발행연도
- 2009.7
- 수록면
- 637 - 640 (4page)
이용수
초록· 키워드
오류제보하기
Kernel methods are ones that, by replacing the inner product with positive definite function, implicitly perform a nonlinear mapping of the input data into a high-dimensional feature space. So far, a large number of vector quantization methods such as Learning vector quantization (LVQ) and Neural gas (NG) with kernel functions have been proposed. But, the performance of them has not always clarified yet. In this paper, we perform some simulations of kernel clusterings with small and large number of weight vectors. As a result, it is show that the performance of LVQ and NG with kernel function are the same as one of them with a large number of weight vectors.
목차
Abstract
1. Introduction
2. Vector quantization clustering with kernel function
3. Vector quantization clustering with kernel function
4. Numerical simulations
5. Conclusions
References
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UCI(KEPA) : I410-ECN-0101-2012-569-004021328