A Risk Minimization Principle for a Class of Parzen Estimators

NeurIPS 2007 Kristiaan PelckmansJohan SuykensBart D. Moor

This paper explores the use of a Maximal Average Margin (MAM) optimality principle for the design of learning algorithms. It is shown that the application of this risk minimization principle results in a class of (computationally) simple learning machines similar to the classical Parzen window classifier... (read more)

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