Conference Proceedings

MASCOT: Fast and Highly Scalable SVM Cross-Validation Using GPUs and SSDs

Z Wen, R Zhang, K Ramamohanarao, J Qi, K Taylor

2014 IEEE International Conference on Data Mining | IEEE | Published : 2014


© 2014 IEEE. Cross-validation is a commonly used method for evaluating the effectiveness of Support Vector Machines (SVMs). However, existing SVM cross-validation algorithms are not scalable to large datasets because they have to (i) hold the whole dataset in memory and/or (ii) perform a very large number of kernel value computation. In this paper, we propose a scheme to dramatically improve the scalability and efficiency of SVM cross-validation through the following key ideas. (i) To avoid holding the whole dataset in the memory and avoid performing repeated kernel value computation, we precompute the kernel values and reuse them. (ii) We store the precomputed kernel values to a high-speed ..

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