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| Smooth Ranking Support Vector Machine Adapting to Web Retrieval |
| HE Hai-Jiang |
| Department of Computer Science and Technology, Changsha University, Changsha 410003 |
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Abstract Cost-sensitive ranking support vector machine converts the order relation of samples into the classification relation of sample pairs, and it is particularly well suited to web information retrieval. However, learning large amounts of sample pairs takes extremely long time. A cost-sensitive smooth ranking support vector machine(cs-sRSVM) using 2-Norm error is presented. Firstly, the optimization object is transformed into unconstrained problem. Secondly, the smooth piecewise polynomial function is approximated to the hinge loss function. Finally, the unique optimal solution is obtained by applying Newton-YUAN method. The experimental results on a public dataset LETOR show that the training time of cs-sRSVM is faster than that of the existing cost-sensitive ranking algorithm, and its retrieval performance is equally impressive.
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Received: 22 September 2008
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2009, Vol. 22 
