We derive risk bounds for the randomized classifiers in Sample Compressions settings where the classifier-specification utilizes two sources of information viz.

We derive risk bounds for the randomized classifiers in Sample Compression set- ting where the classifier-specification utilizes two sources of information ...

We derive risk bounds for the randomized classifiers in Sample Compression settings where the classifier-specification utilizes two sources of information ...

By extending the recently proposed Occam's Hammer principle to the data-dependent settings, point-wise versions of the bounds on the stochastic sample ...

We extend the PAC-Bayes theorem to the sample-compression setting where each clas- sifier is represented by two independent.

Evaluation of Machine Learning algorithms is crucial to both our ability to assess the effectiveness of the proposed approach as well as our understanding ...

Abstract. We propose a PAC-Bayes theorem for the sample-compression setting where each classifier is de- scribed by a compression subset of the training ...

The new PAC-Bayes theorem states that a Gibbs classifler deflned on a posterior over sample- compressed classiflers can have a smaller risk bound than any such ...

Mar 6, 2023 · Randomized predictors are obtained by sampling in a set of basic predictors, ac- cording to some prescribed probability distribution. Thus, ...

risk bounds for majority votes of sample-compressed classifiers, and their associated ... case referred to as true risk, is the probability that a classifier mis- ...