Mittagsseminar (in cooperation with J. Lengler, A. Steger, and D. Steurer)

Date and Time: Thursday, January 15, 2004, 12:15 pm

Duration: This information is not available in the database

Location: This information is not available in the database

Speaker: Simon Spalinger

Supervised Learning of Preference Relations

In this talk we investigate the problem of learning a preference relation from a given set of ranked objects. The main difference between preference learning and standard classification learning is the demand, that the mapping from objects to ranks has to be transitive and antisymmetric. To model this problem we present an approach that performs a linear mapping from objects to scalar utility values and thus guarantees transitivity and antisymmetry. The learning of the preference between objects is formulated as a classification problem on pairs of objects and is solved using the principle of structural risk minimization. The approach is extended to nonlinear utility functions by using the potential function method (the so called kernel trick), which allows to incorporate higher order correlations of features into the utility function at minimal computational costs.

This talk is based on a paper by Ralf Herbrich, Thore Graepel, Peter Bollmann-Sdorra and Klaus Obermayer

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