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Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities
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“This is the case of [248] and [247] , which are studies where SVMs are interpreted as MAP (Maximum A Posteriori) solutions to inference problems with Gaussian Process priors.”
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Metadata
- Authors
- Sollich, P.
- Venue
- Machine Learning
- Published
- 2002-01
- Volume
- 46
- Issue
- 1-3
- Pages
- 21-52
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1567323455
- Cited by
- 187
- References
- 0
- FWCI
- 7.70
- Type
- journal article
- Language
- en
- Open access
- bronze
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
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