Abstract
This paper is a continuation of the study of classification learning algorithms generated by regularization schemes associated with Gaussian kernels and general convex loss functions. In previous papers Xiang and Zhou (2009) [5] , Xiang (2010) [7] , it is assumed that the convex loss ϕ has a zero. This excludes some useful loss functions without zero such as the logistic loss ℓ ( t ) = log ( 1 + exp ( − t ) ) . The main purpose of this paper is to conduct error analysis for the classification learning algorithms associated with such loss functions. The learning rates are derived by a novel application of projection operators to overcome the technical difficulty.
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