The nature of statistical learning theory
The nature of statistical learning theory
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
Text classification using string kernels
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Fast support vector machine training and classification on graphics processors
Proceedings of the 25th international conference on Machine learning
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Text categorization is widely used in applications such as spam filtering, identification of document genre, authorship attribution, and automated essay grading. The rapid growth in the amount of text data gives rise to the urgent need for fast text classification algorithms. In this paper, we propose a GPU based SVM solver for large scale text datasets. Using Platt's Sequential Minimal Optimization algorithm, we achieve a speedup of 5-40 times over LibSVM running on a high-end traditional processor. Prediction time based on the paralleled string kernel computing scheme shows 5-90 times faster performance than the CPU based implementation.