Efficiency of Classification Methods Based on Empirical Risk Minimization
نویسندگان
چکیده
EFFICIENCY OF CLASSIFICATION METHODS BASED ON EMPIRICAL RISK MINIMIZATION V. I. Norkin a and M. A. Keyzer b UDC 519:234:24:85 A binary classification problem is reduced to the minimization of convex regularized empirical risk functionals in a reproducing kernel Hilbert space. The solution is searched for in the form of a finite linear combination of kernel support functions (Vapnik’s support vector machines). Risk estimates for a misclassification as a function of the training sample size and other model parameters are obtained.
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