The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Toward efficient agnostic learning
Open accessAbstract
In this paper we initiate an investigation of generalizations of the Probably Approximately Correct (PAC) learning model that attempt to significantly weaken the target function assumptions. The ultimate goal in this direction is informally termed agnostic learning, in which we make virtually no assumptions on the target function. The name derives from the fact that as designers of learning algorithms, we give up the belief that Nature (as represented by the target function) has a simple or succinct explanation.
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Research works printed but not linked: 2
- 10.1023/A:1022648800760 · doi:10.1023/a:1022648800760 · Paper page
- 10.5555/1625135.1625242 · doi:10.5555/1625135.1625242
Cited by
Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999) · Statistical Science · doi:10.1214/ss/1009212519 · Cited by 4,118
(2015) · Cited by 2,857
The Lack of A Priori Distinctions Between Learning Algorithms
Wolpert, D. H. (1996) · Neural Computation · doi:10.1162/neco.1996.8.7.1341 · Cited by 1,647
Property testing and its connection to learning and approximation
Goldreich, O., Goldwasser, S., & Ron, D. (1998) · Journal of the ACM · doi:10.1145/285055.285060 · Cited by 1,020
Cost-sensitive learning by cost-proportionate example weighting
Zadrozny, B., Langford, J., & Abe, N. (2004) · Third IEEE International Conference on Data Mining · doi:10.1109/icdm.2003.1250950 · Cited by 680
Metadata
- Authors
- Kearns, M. J., Schapire, R. E., & Sellie, L. M.
- Venue
- Proceedings of the fifth annual workshop on Computational learning theory
- Publisher
- ACM
- Published
- 1992-07
- Pages
- 341-352
- OpenAlex
- W1982381767
- Cited by
- 330
- References
- 33
- FWCI
- 12.02
- Type
- conference paper
- Language
- en
- Open access
- gold
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- Kearns, M. J., Schapire, R. E., & Sellie, L. M. (1992). Toward efficient agnostic learning. Proceedings of the fifth annual workshop on Computational learning theory. https://doi.org/10.1145/130385.130424
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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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