A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2006; you can also visit the original URL.
The file type is
We show that several important Bayesian bounds studied in machine learning, both in the batch as well as the online setting, arise by an application of a simple compression lemma. In particular, we derive (i) PAC-Bayesian bounds in the batch setting, (ii) Bayesian log-loss bounds and (iii) Bayesian bounded-loss bounds in the online setting using the compression lemma. Although every setting has different semantics for prior, posterior and loss, we show that the core bound argument is the same.doi:10.1145/1143844.1143855 dblp:conf/icml/Banerjee06 fatcat:jxyka3aymzczthybvkboqebjvy