Approximate Minimization of the Regularized Expected Error over Kernel Models

Approximate Minimization of the Regularized Expected Error over Kernel Models

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Article ID: iaor200954184
Country: United States
Volume: 33
Issue: 3
Start Page Number: 747
End Page Number: 756
Publication Date: Aug 2008
Journal: Mathematics of Operations Research
Authors: ,
Abstract:

Learning from data under constraints on model complexity is studied in terms of rates of approximate minimization of the regularized expected error functional. For kernel models with an increasing number n of kernel functions, upper bounds on such rates are derived. The bounds are of the form a/n+b/√n, where a and b depend on the regularization parameter and on properties of the kernel, and of the probability measure defining the expected error. As a special case, estimates of rates of approximate minimization of the regularized empirical error are derived.

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