Predictive distribution matching SVM for multi-domain learning
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
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Support Vector Machine (SVM) is a new learningmethod developed in recent years based on thefoundations of statistical learning theory. By taking atransductive approach instead of an inductive one insupport vector classifiers, the test set can be used as anadditional source of information about margins. Intuitively,we would expect transductive learning to yieldimprovements when the training sets are small or whenthere is a significant deviation between the training andworking set subsamples of the total population. In thispaper, a progressive transductive support vector machineis addressed to extend Joachims' Transductive SVM tohandle different class distributions. It solves the problemof having to estimate the ratio of positive/negativeexamples from the working set. The experimental resultsshow that the algorithm is very promising.