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Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods (1999)
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| Venue: | ADVANCES IN LARGE MARGIN CLASSIFIERS |
| Citations: | 1049 - 0 self |
BibTeX
@INPROCEEDINGS{Platt99probabilisticoutputs,
author = {John C. Platt},
title = {Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods},
booktitle = {ADVANCES IN LARGE MARGIN CLASSIFIERS},
year = {1999},
pages = {61--74},
publisher = {MIT Press}
}
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Abstract
The output of a classifier should be a calibrated posterior probability to enable post-processing. Standard SVMs do not provide such probabilities. One method to create probabilities is to directly train a kernel classifier with a logit link function and a regularized maximum likelihood score. However, training with a maximum likelihood score will produce non-sparse kernel machines. Instead, we train an SVM, then train the parameters of an additional sigmoid function to map the SVM outputs into probabilities. This chapter compares classification error rate and likelihood scores for an SVM plus sigmoid versus a kernel method trained with a regularized likelihood error function. These methods are tested on three data-mining-style data sets. The SVM+sigmoid yields probabilities of comparable quality to the regularized maximum likelihood kernel method, while still retaining the sparseness of the SVM.
Keyphrases
support vector machine regularized likelihood method probabilistic output classification error rate maximum likelihood score comparable quality sigmoid versus data-mining-style data set maximum likelihood kernel method calibrated posterior probability svm sigmoid yield probability standard svms non-sparse kernel machine logit link function additional sigmoid function kernel classifier likelihood score kernel method regularized maximum likelihood score regularized likelihood error function
