Communications of the ACM - Special issue on parallelism
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
A parallel mixture of SVMs for very large scale problems
Neural Computation
A parallel solver for large quadratic programs in training support vector machines
Parallel Computing - Special issue: Parallel computing in numerical optimization
No Unbiased Estimator of the Variance of K-Fold Cross-Validation
The Journal of Machine Learning Research
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Fast support vector machine training and classification on graphics processors
Proceedings of the 25th international conference on Machine learning
Parallel multiclass classification using SVMs on GPUs
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
A fast parallel optimization for training support vector machine
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Fast Modular network implementation for support vector machines
IEEE Transactions on Neural Networks
Parallel sequential minimal optimization for the training of support vector machines
IEEE Transactions on Neural Networks
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The Support Vector Machine (SVM) is an efficient tool in machine learning with high accuracy performance. However, in order to achieve the highest accuracy performance, n-fold cross validation is commonly used to identify the best hyperparameters for SVM. This becomes a weak point of SVM due to the extremely long training time for various hyperparameters of different kernel functions. In this paper, a novel parallel SVM training implementation is proposed to accelerate the cross validation procedure by running multiple training tasks simultaneously on a Graphics Processing Unit (GPU). All of these tasks with different hyperparameters share the same cache memory which stores the kernel matrix of the support vectors. Therefore, this heavily reduces redundant computations of kernel values across different training tasks. Considering that the computations of kernel values are the most time consuming operations in SVM training, the total time cost of the cross validation procedure decreases significantly. The experimental tests indicate that the time cost for the multitask cross validation training is very close to the time cost of the slowest task trained alone. Comparison tests have shown that the proposed method is 10 to 100 times faster compared to the state of the art LIBSVM tool.