The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Prediction, Learning, and Games
Abstract
This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections.
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Citations
Cites
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A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
Factor graphs and the sum-product algorithm
Kschischang, F. R., Frey, B. J., & Loeliger, H.-A. (2001) · IEEE Transactions on Information Theory · doi:10.1109/18.910572 · Cited by 6,486 · cited 1×
“The dynamic programming implementations of the forecasters for tree experts and for the shortest path problem, both involving computations of sums of products over the nodes of a directed acyclic graph, are special cases of the general sum–product algorithm of Kschischang, Frey, and Loeliger [187].”
MethodFinite-time Analysis of the Multiarmed Bandit Problem
Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002) · Machine Learning · doi:10.1023/a:1013689704352 · Cited by 6,033
Self-citationWIDROW, B., & HOFF, M. E. (1960) · doi:10.21236/ad0241531 · Cited by 4,009
Some aspects of the sequential design of experiments
Robbins, H. (1952) · Bulletin of the American Mathematical Society · doi:10.1090/s0002-9904-1952-09620-8 · Cited by 2,184
A formal theory of inductive inference. Part II
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Subjectivity and Correlation in Randomized Strategies
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Dawid, A. P. (1982) · Journal of the American Statistical Association · doi:10.1080/01621459.1982.10477856 · Cited by 625 · cited 1×
“It was Dawid [80] who introduced the problem to the literature of mathematical statistics – see also Dawid’s related work on calibration and “prequential” statistics [81, 82, 84].”
Background
Cited by
A survey on concept drift adaptation
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014) · ACM Computing Surveys · doi:10.1145/2523813 · Cited by 3,735
Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I. P., & Tygar, J. D. (2011) · Proceedings of the 4th ACM workshop on Security and artificial intelligence · doi:10.1145/2046684.2046692 · Cited by 1,194
Foundations of Machine Learning
Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018) · doi:10.7551/mitpress/10290.001.0001 · Cited by 1,122
Philosophy and the practice of Bayesian statistics
Gelman, A., & Shalizi, C. R. (2013) · British Journal of Mathematical and Statistical Psychology · doi:10.1111/j.2044-8317.2011.02037.x · Cited by 825
Popularity versus similarity in growing networks
Papadopoulos, F., Kitsak, M., Serrano, M. Á., Boguñá, M., & Krioukov, D. (2012) · Nature · doi:10.1038/nature11459 · Cited by 634
Metadata
- Authors
- Cesa-Bianchi, N., & Lugosi, G.
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2006-03-13
- OpenAlex
- W1570963478
- Cited by
- 3,162
- References
- 11
- FWCI
- 59.54
- Type
- book
- Language
- en
- Open access
- closed
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
