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Using and combining predictors that specialize
Open accessAbstract
We study online learning algorithms that predict by combining the predictions of severrd subordinate prediction algorithms, sometimes crdled "experts ."These simple algorithms belong to the multiplicative weights family of algorithms.The performance of these algorithms degrades only logarithmically with the number of experts, making them particularly useful in applications where the number of experts is very large.However, in applications such as text categorization, it is often natural for some of the experts to abstain from making predictions on some of the instances.We show how to transform algorithms that assume that afl experts are atways awake to algorithms that do not require this assumption.We also show how to derive corresponding Ioss bounds.Our method is very generaf, and can be applied to a large family of online learning algori[hms.We also give applications to various prediction models including decision graphs and "switching" experts.
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Cited by
Prediction, Learning, and Games
Cesa-Bianchi, N., & Lugosi, G. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511546921 · Cited by 3,162
Herbster, M., & Warmuth, M. K. (1998) · Machine Learning · doi:10.1023/a:1007424614876 · Cited by 560
Self-citationContext-sensitive learning methods for text categorization
Cohen, W. W., & Singer, Y. (1999) · ACM Transactions on Information Systems · doi:10.1145/306686.306688 · Cited by 360
Self-citationBoosting and Rocchio applied to text filtering
Schapire, R. E., Singer, Y., & Singhal, A. (1998) · Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/290941.290996 · Cited by 301
Self-citationBlum, L., & Blum, M. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2115934119 · Cited by 70
Metadata
- Authors
- Freund, Y., Schapire, R. E., Singer, Y., & Warmuth, M. K.
- Venue
- Proceedings of the twenty-ninth annual ACM symposium on Theory of computing - STOC '97
- Publisher
- ACM Press
- Published
- 1997
- Pages
- 334-343
- OpenAlex
- W2046934276
- Cited by
- 234
- References
- 0
- FWCI
- 25.00
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- CoChat. (2026). Citation check: Using and combining predictors that specialize. https://app.cochat.ai/papers/10.1145/258533.258616
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- Freund, Y., Schapire, R. E., Singer, Y., & Warmuth, M. K. (1997). Using and combining predictors that specialize. Proceedings of the twenty-ninth annual ACM symposium on Theory of computing - STOC '97. https://doi.org/10.1145/258533.258616
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