Support vector machines, reproducing kernel Hilbert spaces, and randomized GACV
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
SVM selective sampling for ranking with application to data retrieval
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Adapting ranking SVM to document retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Enabling soft queries for data retrieval
Information Systems
Nomogram visualization for ranking support vector machine
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
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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.