Risk Bounds for Randomized Sample Compressed Classifiers

Part of Advances in Neural Information Processing Systems 21 (NIPS 2008)

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Mohak Shah


We derive risk bounds for the randomized classifiers in Sample Compressions settings where the classifier-specification utilizes two sources of information viz. the compression set and the message string. By extending the recently proposed Occam’s Hammer principle to the data-dependent settings, we derive point-wise versions of the bounds on the stochastic sample compressed classifiers and also recover the corresponding classical PAC-Bayes bound. We further show how these compare favorably to the existing results.