On data based learning using support vector clustering

This paper addresses the effect of applying clustering algorithms, based on a distance metric rule, prior to support kernel learning in classification and regression problems. Self-Organising Maps (SOMs), which place emphasis in data domain description, and K-means clustering algorithms have been selected before applying a support vector algorithm which is based on a margin rule. Moreover, the recently developed support vector clustering algorithm, based on a cluster boundary rule, is applied in benchmark problems for comparison.