Kernel Polarization Based on Cooperative Clustering

In recent years, kernel methods are used in many applications, such as text classification and gene recognition. The parameters of kernels are empirically decided by the context of application. In order to select the appropriate kernel parameters, kernel polarization is presented as a universal kernel optimality criterion, which is independent of the classifier to be used. However, kernel polarization has several disadvantages, leading to the inconvenience of applying such method. In this paper, a clustering algorithm called Cooperative Clustering is integrated with kernel polarization. The experimental results showed the effectiveness of the approach.

[1]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[2]  Nello Cristianini,et al.  Dynamically Adapting Kernels in Support Vector Machines , 1998, NIPS.

[3]  Shaomin Mu,et al.  Cooperative Clustering for Training SVMs , 2006, ISNN.

[4]  Joshua Zhexue Huang,et al.  Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values , 1998, Data Mining and Knowledge Discovery.

[5]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2003, ICTAI.

[6]  Yoram Baram,et al.  Learning by Kernel Polarization , 2005, Neural Computation.

[7]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[8]  Jacek M. Zurada,et al.  Advances in Neural Networks - ISNN 2006, Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part I , 2006, International Symposium on Neural Networks.

[10]  Martin D. Buhmann,et al.  Radial Basis Functions , 2021, Encyclopedia of Mathematical Geosciences.

[11]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[12]  Chih-Jen Lin,et al.  Radius Margin Bounds for Support Vector Machines with the RBF Kernel , 2002, Neural Computation.

[13]  Christian Igel,et al.  Gradient-Based Adaptation of General Gaussian Kernels , 2005, Neural Computation.

[14]  Huang,et al.  Learning General Gaussian Kernels by Optimizing Kernel Polarization , 2009 .