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http://dx.doi.org/10.3745/KIPSTB.2006.13B.4.439

A New Self-Organizing Map based on Kernel Concepts  

Cheong Sung-Moon (국가보안기술연구소)
Kim Ki-Bom (국가보안기술연구소)
Hong Soon-Jwa (국가보안기술연구소)
Publication Information
The KIPS Transactions:PartB / v.13B, no.4, 2006 , pp. 439-448 More about this Journal
Abstract
Previous recognition/clustering algorithms such as Kohonen SOM(Self-Organizing Map), MLP(Multi-Layer Percecptron) and SVM(Support Vector Machine) might not adapt to unexpected input pattern. And it's recognition rate depends highly on the complexity of own training patterns. We could make up for and improve the weak points with lowering complexity of original problem without losing original characteristics. There are so many ways to lower complexity of the problem, and we chose a kernel concepts as an approach to do it. In this paper, using a kernel concepts, original data are mapped to hyper-dimension space which is near infinite dimension. Therefore, transferred data into the hyper-dimension are distributed spasely rather than originally distributed so as to guarantee the rate to be risen. Estimating ratio of recognition is based on a new similarity-probing and learning method that are proposed in this paper. Using CEDAR DB which data is written in cursive letters, 0 to 9, we compare a recognition/clustering performance of kSOM that is proposed in this paper with previous SOM.
Keywords
SOM; Self-Organizing; Kernel Concept; Learning;
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  • Reference
1 Y.A. LeCun, et. al., 'Learning algorithms for classification: A comparison on handwritten digit recognition', Neural Networks, 1995
2 G. Baudat and F. Anouar, 'Generalized discriminant analysis using a kernel approach', Neural Computation, Vol.12, No.10, pp.2385-2404, 2000   DOI
3 B. Scholkopf, A.J, Smola, and K-R. Muller, 'Nonlinear component analysis as a kernel eigenvalue problem', Neural Computation, Vol.10, pp.1299-1319, 1998   DOI   ScienceOn
4 C.J.C. Burges, 'A Tutorial on Support Vector Machines for Pattern Recognition', Data Mining and Knowledge Discovery, Vol.2, pp.121-167, 1998   DOI   ScienceOn
5 Bernhard Scholkopf, et. al., 'Input Space vs. Feature Space in Kernel Based Methods', IEEE Transactions on Neural Netwoks, Vol.10, No.5, pp.1000-1017, 1999   DOI   ScienceOn
6 B. Scholkopf, 'Support vector learning', Oldenbourg Verlag, Munich, 1997
7 V.N. Vapnik, 'The nature of statistical learning theory', Springer Verlag, New York, 1995
8 V.N. Vapnik, 'Statistical Learning Theory', Wiley, New York, 1998
9 K-R. Muller, et al., 'An Intoduction to Kernel-Based Learning Algorithms', IEEE Transactions on Neural Netwoks, Vol.12, No.2, March, 2001   DOI   ScienceOn
10 Simon Haykin, 'Neural Networks', Prentice Hall, 2nd Ed., 1999
11 Sungmoon Cheong, Sang Hoon Oh and Soo- Young Lee, 'Support Vector Machines with Binary Tree Achitecture for Multi-Class Classification', Neural Information Processing Letters and Reviews, Vol.2, No.3, March, 2004