The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
A Game of Prediction with Expert Advice
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Proceedings of the 25th international conference on Machine learning
Cohen, W. W., McCallum, A., & Roweis, S. T. (2008) · Cited by 5,691
The Nature of Statistical Learning Theory
Sain, S. R. (1996) · Technometrics · doi:10.1080/00401706.1996.10484565 · Cited by 5,434
An Introduction to Probability Theory and its Applications.
Kingman, J. F. C., & Feller, W. (1972) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2344620 · Cited by 4,938
The Weighted Majority Algorithm
Littlestone, N., & Warmuth, M. K. (1994) · Information and Computation · doi:10.1006/inco.1994.1009 · Cited by 1,975
Williams, D. (1991) · Cambridge University Press eBooks · doi:10.1017/cbo9780511813658 · Cited by 1,691
A Universal Prior for Integers and Estimation by Minimum Description Length
Rissanen, J. (1983) · The Annals of Statistics · doi:10.1214/aos/1176346150 · Cited by 1,672
Dawid, A. P. (1984) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2981683 · Cited by 1,085
Exponentiated Gradient versus Gradient Descent for Linear Predictors
Kivinen, J., & Warmuth, M. K. (1997) · Information and Computation · doi:10.1006/inco.1996.2612 · Cited by 864
Proceedings. 45th Annual IEEE Symposium on Foundations of Computer Science
(2004) · 45th Annual IEEE Symposium on Foundations of Computer Science · doi:10.1109/focs.2004.4 · Cited by 805
Encyclopedia of Statistical Sciences
(2004) · Wiley eBooks · doi:10.1002/0471667196 · Cited by 724
Cesa-Bianchi, N., Freund, Y., Haussler, D., Helmbold, D. P., Schapire, R. E., & Warmuth, M. K. (1997) · Journal of the ACM · doi:10.1145/258128.258179 · Cited by 634
Herbster, M., & Warmuth, M. K. (1998) · Machine Learning · doi:10.1023/a:1007424614876 · Cited by 560
Estimation and Inference by Compact Coding
Wallace, C. S., & Freeman, P. R. (1987) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1987.tb01695.x · Cited by 523
Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm
Littlestone, N. (1988) · Machine Learning · doi:10.1007/bf00116827 · Cited by 497
Universal portfolios with side information
Cover, T. M., & Ordentlich, E. (1996) · IEEE Transactions on Information Theory · doi:10.1109/18.485708 · Cited by 347
Rissanen, J. (1987) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1987.tb01694.x · Cited by 339
Tight worst-case loss bounds for predicting with expert advice
Haussler, D. H., Kivinen, J., & Warmuth, M. K. (1995) · Lecture notes in computer science · doi:10.1007/3-540-59119-2_169 · Cited by 88
Kearns, M. J., & Vazirani, U. V. (1995) · ACM SIGACT News · doi:10.1145/203610.606411 · Cited by 65
A Randomization Rule for Selecting Forecasts
Foster, D. P., & Vohra, R. (1993) · Operations Research · doi:10.1287/opre.41.4.704 · Cited by 58
Universal forecasting algorithms
Vovk, V. (1992) · Information and Computation · doi:10.1016/0890-5401(92)90050-p · Cited by 45
Self-citationOn-line learning of linear functions
Littlestone, N., Warmuth, M. K., & Long, P. M. (1995) · Computational Complexity · doi:10.1007/bf01277953 · Cited by 29
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
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
The Fundamental Nature of the Log Loss Function
Vovk, V. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-23534-9_20 · Cited by 71
Self-citation
Metadata
- Authors
- Vovk, V
- Venue
- Journal of Computer and System Sciences
- Publisher
- Elsevier BV
- Published
- 1998-04
- Volume
- 56
- Issue
- 2
- Pages
- 153-173
- ISSN
- 0022-0000 (ISSN-L)
- OpenAlex
- W3149261725
- Cited by
- 281
- References
- 22
- FWCI
- 16.13
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
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.
