Author: Marcus Hutter (2004)
Comments: 4 pages
Subj-class: Learning; Artificial Intelligence
BibTeX Entry
@TechReport{Hutter:04bayespea,
author = "M. Hutter",
title = "Online Prediction – Bayes versus Experts",
month = jul,
year = "2004",
note = "Presented at the EU PASCAL Workshop on
Learning Theoretic and Bayesian Inductive Principles (LTBIP-2004)",
url = "http://www.hutter1.net/ai/bayespea.htm",
ps = "http://www.hutter1.net/ai/bayespea.ps",
pdf = "http://www.hutter1.net/ai/bayespea.pdf",
slides = "http://www.hutter1.net/ai/sbayespea.pdf",
keywords = "Bayesian sequence prediction;
Prediction with Expert Advice;
general weights, alphabet and loss.",
abstract = "We derive a very general regret bound in the framework of prediction with expert advice, which challenges the best known regret bound for Bayesian sequence prediction. Both bounds of the form
$\sqrt{\mbox{Loss}\times\mbox{complexity}}$
hold for any bounded loss-function, any prediction and observation spaces, arbitrary expert/environment classes and weights, and unknown sequence length.",
http://www.hutter1.net/ai/bayespea.htm