Support vector machine (SVM) is a promising
technique for data classification
and regression.
However, it provides only decision values but not posterior probability
estimates.
As many applications require probability outputs,
it is essential to study how to transform SVM outputs to probability values.
In this thesis, we study and compare various methods.
