1 research outputs found
Handling uncertainties in SVM classification
This paper addresses the pattern classification problem arising when
available target data include some uncertainty information. Target data
considered here is either qualitative (a class label) or quantitative (an
estimation of the posterior probability). Our main contribution is a SVM
inspired formulation of this problem allowing to take into account class label
through a hinge loss as well as probability estimates using epsilon-insensitive
cost function together with a minimum norm (maximum margin) objective. This
formulation shows a dual form leading to a quadratic problem and allows the use
of a representer theorem and associated kernel. The solution provided can be
used for both decision and posterior probability estimation. Based on empirical
evidence our method outperforms regular SVM in terms of probability predictions
and classification performances.Comment: IEEE Workshop on Statistical Signal Processing, Nice: France (2011