Risk Bounds for Randomized Sample Compressed Classifiers

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

Authors

Mohak Shah

Abstract

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.