The weak aggregating algorithm and weak mixability

Authors:
Yuri Kalnishkan;Michael V. Vyugin
Affiliations:
Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, UK;Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, UK
Venue:
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Year:
2005

Citing 4
Cited 4

Aggregating strategies

COLT '90 Proceedings of the third annual workshop on Computational learning theory
How to use expert advice

Journal of the ACM (JACM)
A game of prediction with expert advice

Journal of Computer and System Sciences - Special issue on the eighth annual workshop on computational learning theory, July 5–8, 1995
Sequential prediction of individual sequences under general loss functions

IEEE Transactions on Information Theory

Prediction with expert advice for the Brier game

Proceedings of the 25th international conference on Machine learning
Generalised entropy and asymptotic complexities of languages

COLT'07 Proceedings of the 20th annual conference on Learning theory
Competing with stationary prediction strategies

COLT'07 Proceedings of the 20th annual conference on Learning theory
Defensive prediction with expert advice

ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper resolves the problem of predicting as well as the best expert up to an additive term o(n), where n is the length of a sequence of letters from a finite alphabet. For the bounded games the paper introduces the Weak Aggregating Algorithm that allows us to obtain additive terms of the form $C{\sqrt n}$. A modification of the Weak Aggregating Algorithm that covers unbounded games is also described.