Fast training of support vector machines using sequential minimal optimization
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
Modern Information Retrieval
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A Generalized Representer Theorem
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
An introduction to variable and feature selection
The Journal of Machine Learning Research
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
A Feature Selection Newton Method for Support Vector Machine Classification
Computational Optimization and Applications
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
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Adapting ranking SVM to document retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Enabling soft queries for data retrieval
Information Systems
Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization
The Journal of Machine Learning Research
Feature selection in a kernel space
Proceedings of the 24th international conference on Machine learning
Ranking with multiple hyperplanes
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Information Technology in Biomedicine
RankSVR: can preference data help regression?
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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Learning ranking (or preference) functions has become an important data mining task in recent years, as various applications have been found in information retrieval. Among rank learning methods, ranking SVM has been favorably applied to various applications, e.g., optimizing search engines, improving data retrieval quality. In this paper, we first develop a 1-norm ranking SVM that is faster in testing than the standard ranking SVM, and propose Ranking Vector SVM (RV-SVM) that revises the 1-norm ranking SVM for faster training . The number of variables in the RV-SVM is significantly smaller, thus the RV-SVM trains much faster than the other ranking SVMs. We experimentally compared the RV-SVM with the state-of-the-art rank learning method provided in SVM-light. The RV-SVM uses much less support vectors and trains much faster for nonlinear kernels than the SVM-light. The accuracies of RV-SVM and SVM-light are comparable on relatively large data sets. Our implementation of RV-SVM is posted at http://iis.postech.ac.kr/rv-svm.