SVMTorch
SVMTorch: Support vector machines for large-scale regression problems. Support Vector Machines (SVMs) for regression problems are trained by solving a quadratic optimization problem which needs on the order of l2 memory and time resources to solve, where l is the number of training examples. In this paper, we propose a decomposition algorithm, SVMTorch, which is similar to SVM-Light proposed by T. Joachims [“Making large-scale support vector machine learning practical”, in: B. Schölkopf, C. Burges, and A. Smola (eds.), Advances in kernel methods. London: MIT Press (1998; Zbl 0935.68084)] for classification problems, but adapted to regression problems. With this algorithm, one can now efficiently solve large-scale regression problems (more than 20000 examples). Comparisons with Nodelib, another publicly available SVM algorithm for large-scale regression problems from G. Flake and S. Lawrence [Mach. Learn. 46, 271–290 (2002; Zbl 0998.68107)] yielded significant time improvements. Finally, based on a recent paper from C. Lin [On the convergence of the decomposition method for support vector machines (Tech. Rep.). National Taiwan University (2000)], we show that a convergence proof exists for our algorithm.
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References in zbMATH (referenced in 61 articles , 1 standard article )
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