Making large-scale support vector machine learning practical
Advances in kernel methods
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Lagrangian support vector machines
The Journal of Machine Learning Research
Solving large scale linear prediction problems using stochastic gradient descent algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
Training a Support Vector Machine in the Primal
Neural Computation
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
A scalable modular convex solver for regularized risk minimization
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Regularized bundle methods for convex and non-convex risks
The Journal of Machine Learning Research
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We propose a new algorithm for training a linear Support Vector Machine in the primal. The algorithm mixes ideas from non smooth optimization, subgradient methods, and cutting planes methods. This yields a fast algorithm that compares well to state of the art algorithms. It is proved to require O(1/茂戮驴茂戮驴) iterations to converge to a solution with accuracy 茂戮驴. Additionally we provide an exact shrinking method in the primal that allows reducing the complexity of an iteration to much less than O(N) where Nis the number of training samples.