期刊名称：International Journal on Computer Science and Engineering
印刷版ISSN：2229-5631
电子版ISSN：0975-3397
出版年度：2011
卷号：3
期号：10
页码：3355-3358
出版社：Engg Journals Publications
摘要：Spectral clustering and Leader�s algorithm have both been used to identify clusters that are nonlinearly separable in input space. Despite significant research, these methods have remained only loosely related. Sigmoid kernel and polynomial kernel were quite popular for support vector machines due to its origin from clustering. In this paper we are submitting the comparison of above kernel methods after reducing the dimensions using feature functions. For this we have given hand writing data -sets to create and compare the clusters.
关键词：Sigmoid; polynomial; kernels; Support Vector Machine and leader�s algorithm.
