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Thursday 29 March 2007, 13:00-14:00
Reduced kernel rules for classification
- 👤 Speaker: Frederic Desobry, Sigproc. Lab. CUED
- 📅 Date & Time: Thursday 29 March 2007, 13:00 - 14:00
- 📍 Venue: LR5, Engineering, Department of
Abstract
A new methodology is proposed to discriminate between two probability measures known through a set of data distributed according to either of these two measures. A decision rule is built as the plug-in of a kernel rule, defined on a small subset of the learning set. This methodology allows for fast yet accurate estimates of the optimal classification rule. A statistical analysis yields consistency results, and rates of convergence for the probability of error. A dedicated model selection procedure is described, and experiments illustrate further the comparison to state-of-the art classifiers.
Series This talk is part of the Probabilistic Systems, Information, and Inference Group Seminars series.
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