The DOI matches its Crossref record.
Functional Gradient Techniques for Combining Hypotheses
Abstract
This chapter contains sections titled: Introduction, Optimizing Cost Functions of the Margin, A Gradient Descent View of Voting Methods, Theoretically Motivated Cost Functions, Convergence Results, Experiments, Conclusions, Acknowledgments
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Cited by
Classification in the Presence of Label Noise: A Survey
Frenay, B., & Verleysen, M. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2292894 · Cited by 1,777
A survey of appearance models in visual object tracking
Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., & Hengel, A. V. D. (2013) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2508037.2508039 · Cited by 705
FloatBoost learning and statistical face detection
Li, S. Z., & Zhang, Z. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.68 · Cited by 525
Metadata
- Authors
- Mason, L., Baxter, J., Bartlett, P. L., & Frean, M.
- Venue
- Advances in Large-Margin Classifiers
- Publisher
- The MIT Press
- Published
- 2000-09-29
- Pages
- 221-246
- OpenAlex
- W1570060426
- Cited by
- 279
- References
- 0
- FWCI
- 32.92
- Type
- book chapter
- Language
- en
- Open access
- closed
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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.
