Description:

SVM are efficient discriminative classifiers but they cannot be applied when the learning set consists of both certain labels {-1,1} and uncertain labels represented by a posterior probability estimate (0,1).

We address this problem in our SSP 2011 paper entitled HANDLING UNCERTAINTIES IN SVM CLASSIFICATION. Basically we learn a unique classifier satisfying both classification performances on the certain labels and performs a probabilistic regression on the uncertain labels. Our approach proved efficient in terms of classification performances and probabilistic output compared to a classical Platt estimation.

This package contains our paper, a matlab function that learn from uncertain labels instead of certain ones (usvmclass.m), and 3 test scripts corresponding to the numerical experiments in the paper (test*.m).

Changes to previous version:

Added missing dataset function (thanks to Hao Wu)

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Platform Independent

Data Formats: Matlab

Tags: Svm, Kernel Methods, Algorithm, Probability Estimation

Archive: download here

Other available revisons

Version	Changelog	Date
0.2	Added missing dataset function (thanks to Hao Wu)	July 17, 2012, 11:06:23
0.1	Initial Announcement on mloss.org.	July 6, 2011, 23:12:21

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