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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2015

01.12.2015

Learning to Rank with Ensemble Ranking SVM

verfasst von: Cheolkon Jung, Yanbo Shen, Licheng Jiao

Erschienen in: Neural Processing Letters | Ausgabe 3/2015

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Abstract

In this paper, we propose a novel learning to rank method using Ensemble Ranking SVM. Ensemble Ranking SVM is based on Ranking SVM which has been commonly used for learning to rank. The basic idea of Ranking SVM is to formulate the problem of learning to rank as that of binary classification on instance pairs. In Ranking SVM, the training time of generating a train model grows exponentially as the training data set increases in size. To solve this problem and improve the ranking accuracy, we introduce ensemble learning into Ranking SVM. Therefore, Ensemble Ranking SVM remarkably improves the efficiency of the model training as well as achieves high ranking accuracy. Experimental results demonstrate that the performance of Ensemble Ranking SVM is quite impressive from the viewpoints of ranking accuracy and training time.
Vorheriger Artikel Almost Automorphic Solutions of Impulsive Cellular Neural Networks with Piecewise Constant Argument
Nächster Artikel An Efficient and Effective Multiple Empirical Kernel Learning Based on Random Projection

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Metadaten
Titel
Learning to Rank with Ensemble Ranking SVM
verfasst von
Cheolkon Jung
Yanbo Shen
Licheng Jiao
Publikationsdatum
01.12.2015
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2015
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-014-9382-5

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