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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Probabilistic Ranking Support Vector Machine
verfasst von : Nguyen Thi Thanh Thuy, Ngo Anh Vien, Nguyen Hoang Viet, TaeChoong Chung
Erschienen in: Advances in Neural Networks – ISNN 2009
Verlag: Springer Berlin Heidelberg
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Recently, Support Vector Machines (SVMs) have been applied very effectively in learning ranking functions (or preference functions).They intend to learn ranking functions with the principles of the
large margin
and the
kernel trick
. However, the output of a ranking function is a score function which is not a calibrated posterior probability to enable post-processing. One approach to deal with this problem is to apply a generalized linear model with a link function and solve it by calculating the maximum likelihood estimate. But, if the link function is nonlinear, maximizing the likelihood will face with difficulties. Instead, we propose a new approach which train an SVM for a ranking function, then map the SVM outputs into a probabilistic sigmoid function whose parameters are trained by using cross-validation. This method will be tested on three data-mining datasets and compared to the results obtained by standard SVMs.