A Risk Minimization Principle for a Class of Parzen Estimators

This paper1 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. A direct relation with the Rademacher complexities is established, as such facilitating analysis and providing a notion of certainty of prediction. This analysis is related to Support Vector Machines by means of a margin transformation. The power of the MAM principle is illustrated further by application to ordinal regression tasks, resulting in an O(n) algorithm able to process large datasets in reasonable time.

• Type: Conference
• Year: 2007
• Where: NIPS
• Authors: Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
• Keywords: Information Technology, Maximal Average Margin, NIPS 2007, Optimality Principle, Risk Minimization Principle