Unsupervised clustering and the capacity of support vector machines

In the framework of support vector machine (SVM) classifiers, an unsupervised analysis of empirical data supports an ordering criterion for the families of possible functions. The approach enhances the structural risk minimization paradigm by sharply reducing the number of admissible classifiers, thus tightening the associate generalization bound. The paper shows that kernel-based algorithms, allowing efficient optimization, can support both the unsupervised clustering process and the generalization-error estimation. The main result of this sample-based method may be a dramatic reduction in the predicted generalization error, as demonstrated by experiments on synthetic testbeds as well as real-world problems.