Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods

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Summary

The output of a lassi(cid:12)er should be a alibrated posterior probability to enable post-pro essing and a method to train a kernel lassi with a logit link and a regularized maximum likelihood is proposed.

Type
article
Published
1999-01-01
Cited by
7,144
References
21

Keywords

Support vector machine, Probabilistic logic, Computer science, Artificial intelligence, Machine learning

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