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Reduced kernel rules for classification

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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.

This talk is part of the Signal Processing and Communications Lab Seminars series.

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