How to Use Expert Advice

DMCA

How to Use Expert Advice (1997)

Cached

Download Links

by Nicolò Cesa-Bianchi , Yoav Freund , David Haussler , David P. Helmbold , Robert E. Schapire , Manfred K. Warmuth

Venue:	JOURNAL OF THE ASSOCIATION FOR COMPUTING MACHINERY
Citations:	377 - 79 self

BibTeX

@MISC{Cesa-Bianchi97howto,
    author = {Nicolò Cesa-Bianchi and Yoav Freund and David Haussler and David P. Helmbold and Robert E. Schapire and Manfred K. Warmuth},
    title = { How to Use Expert Advice},
    year = {1997}
}

OpenURL

Abstract

We analyze algorithms that predict a binary value by combining the predictions of several prediction strategies, called experts. Our analysis is for worst-case situations, i.e., we make no assumptions about the way the sequence of bits to be predicted is generated. We measure the performance of the algorithm by the difference between the expected number of mistakes it makes on the bit sequence and the expected number of mistakes made by the best expert on this sequence, where the expectation is taken with respect to the randomization in the predictions. We show that the minimum achievable difference is on the order of the square root of the number of mistakes of the best expert, and we give efficient algorithms that achieve this. Our upper and lower bounds have matching leading constants in most cases. We then show howthis leads to certain kinds of pattern recognition/learning algorithms with performance bounds that improve on the best results currently known in this context. We also compare our analysis to the case in which log loss is used instead of the expected number of mistakes.

Keyphrases

expected number expert advice certain kind binary value square root bit sequence pattern recognition minimum achievable difference howthis lead performance bound several prediction strategy efficient algorithm worst-case situation log loss

DMCA