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
Information Sciences: an International Journal
A rough margin based support vector machine
Information Sciences: an International Journal
Induction of multiple fuzzy decision trees based on rough set technique
Information Sciences: an International Journal
Improving generalization of fuzzy IF-THEN rules by maximizing fuzzy entropy
IEEE Transactions on Fuzzy Systems
Simultaneous feature selection and classification using kernel-penalized support vector machines
Information Sciences: an International Journal
18F-FDG PET imaging analysis for computer aided Alzheimer's diagnosis
Information Sciences: an International Journal
Exact indexing for support vector machines
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
IEEE Transactions on Information Technology in Biomedicine
Maximum Ambiguity-Based Sample Selection in Fuzzy Decision Tree Induction
IEEE Transactions on Knowledge and Data Engineering
Multi-appliance recognition system with hybrid SVM/GMM classifier in ubiquitous smart home
Information Sciences: an International Journal
Probabilistic generative ranking method based on multi-support vector domain description
Information Sciences: an International Journal
Automatic field data analyzer for closed-loop vehicle design
Information Sciences: an International Journal
| Hi-index | 0.07 |
The problem of learning ranking (or preference) functions has become important in recent years as various applications have been found in information retrieval. Among the rank learning methods, RankSVM has been favorably used in various applications, e.g., optimizing search engines and improving data retrieval quality. Fast learning methods for linear RankSVM (RankSVM with a linear kernel) have been extensively developed, whereas methods for nonlinear RankSVM (RankSVM with nonlinear kernels) are lacking. This paper proposes an efficient method for learning with nonlinear kernels, called Ranking Vector SVM (RV-SVM). RV-SVM utilizes training vectors rather than pairwise difference vectors to determine the support vectors, and is thus faster to train than conventional RankSVMs. Experimental comparisons with the state-of-the-art RankSVM implementation provided in SVM-light show that RV-SVM is substantially faster for nonlinear kernels, although our method is slower for linear kernels. RV-SVM also uses far fewer support vectors, and thus the trained models are much simpler than those built by RankSVMs while maintaining comparable accuracy. Our implementation of RV-SVM is accessible at http://dm.hwanjoyu.org/rv-svm.