Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Training a Support Vector Machine in the Primal
Neural Computation
A scalable modular convex solver for regularized risk minimization
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
ROC analysis in ordinal regression learning
Pattern Recognition Letters
An efficient algorithm for learning to rank from preference graphs
Machine Learning
Sparse kernel SVMs via cutting-plane training
Machine Learning
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization
The Journal of Machine Learning Research
Bundle Methods for Regularized Risk Minimization
The Journal of Machine Learning Research
Efficient algorithms for ranking with SVMs
Information Retrieval
An improved training algorithm for the linear ranking support vector machine
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
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We introduce an efficient method for training the linear ranking support vector machine. The method combines cutting plane optimization with red-black tree based approach to subgradient calculations, and has O(ms+mlog (m)) time complexity, where m is the number of training examples, and s the average number of non-zero features per example. Best previously known training algorithms achieve the same efficiency only for restricted special cases, whereas the proposed approach allows any real valued utility scores in the training data. Experiments demonstrate the superior scalability of the proposed approach, when compared to the fastest existing RankSVM implementations.