On the influence of the kernel on the generalization ability of support vector machines

In this article we study the generalization abilities of several classifiers of support vector machine type. Our considerations are based on an investigation of certain approximation properties of the used kernels which also gives a new insight into the role of kernels in these and other algorithms. For deterministic supervisors we derive estimates on the generalization performance which are asymptotically sharper than all known results. Moreover, for supervisors which are corrupted by a certain kind of noise we show that the support vector approach yields acceptable generalization results.