The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
How to use expert advice
Open accessAbstract
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 predictins. 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 how this leads to certain kinds of pattern recognition/learning algorithms with performance bounds that improve on the best results currently know in this context. We also compare our analysis to the case in which log loss is used instead of the expected number of mistakes.
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Cites
Valiant, L. G. (1984) · Communications of the ACM · doi:10.1145/1968.1972 · Cited by 3,279
Dawid, A. P. (1984) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2981683 · Cited by 1,085 · cited 1×
“We take the extreme position, as advocated by Dawid and Vovk in the theory of prequential probability [Dawid 1984; 1991; 1996; Vovk 1993], Rissanen in his theory of stochastic complexity [Rissanen 1978; 1986; Rissanen and Langdon Jr. 1981; Yamanishi 1995], and Cover, Lempel and Ziv, Feder and…”
BackgroundFiat, A., Karp, R. M., Luby, M., McGeoch, L. A., Sleator, D. D., & Young, N. E. (1991) · Journal of Algorithms · doi:10.1016/0196-6774(91)90041-v · Cited by 457
Cited by
Prediction, Learning, and Games
Cesa-Bianchi, N., & Lugosi, G. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511546921 · Cited by 3,162
Self-citation(2002) · doi:10.1137/s0097539701398375 · Cited by 2,275
Foundations of Machine Learning
Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018) · doi:10.7551/mitpress/10290.001.0001 · Cited by 1,122
Large margin classification using the perceptron algorithm
Freund, Y., & Schapire, R. E. (1998) · Proceedings of the eleventh annual conference on Computational learning theory · doi:10.1145/279943.279985 · Cited by 1,071
Self-citationCan machine learning be secure?
Barreno, M., Nelson, B., Sears, R., Joseph, A. D., & Tygar, J. D. (2006) · Proceedings of the 2006 ACM Symposium on Information, computer and communications security · doi:10.1145/1128817.1128824 · Cited by 836
Metadata
- Authors
- Cesa-Bianchi, N., Freund, Y., Haussler, D., Helmbold, D. P., Schapire, R. E., & Warmuth, M. K.
- Venue
- Journal of the ACM
- Published
- 1997-05
- Volume
- 44
- Issue
- 3
- Pages
- 427-485
- ISSN
- 0004-5411 (ISSN-L)
- 1557-735X
- OpenAlex
- W1979675141
- Cited by
- 634
- References
- 3
- FWCI
- 26.34
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Cesa-Bianchi, N., Freund, Y., Haussler, D., Helmbold, D. P., Schapire, R. E., & Warmuth, M. K. (1997). How to use expert advice. Journal of the ACM. https://doi.org/10.1145/258128.258179
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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