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A dual coordinate descent method for large-scale linear SVM
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Trust Region Newton Method for Logistic Regression
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Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods
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On updates that constrain the features' connections during learning
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Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines
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EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
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Bundle Methods for Regularized Risk Minimization
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PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Selection of basis functions guided by the L2 soft margin
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
An improved algorithm for the solution of the regularization path of support vector machine
IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
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HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
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Online SVR Training by Solving the Primal Optimization Problem
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Computers and Operations Research
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EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Integrating statistical and lexical information for recognizing textual entailments in text
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Mathematics and Computers in Simulation
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This paper develops a fast method for solving linear SVMs with L2 loss function that is suited for large scale data mining tasks such as text classification. This is done by modifying the finite Newton method of Mangasarian in several ways. Experiments indicate that the method is much faster than decomposition methods such as SVMlight, SMO and BSVM (e.g., 4-100 fold), especially when the number of examples is large. The paper also suggests ways of extending the method to other loss functions such as the modified Huber's loss function and the L1 loss function, and also for solving ordinal regression.