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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  1. Kuang, Wei; Brown, Laura E.; Wang, Zhenlin: Selective switching mechanism in virtual machines via support vector machines and transfer learning (2015)
  2. Chen, Xiaobo; Yang, Jian; Chen, Long: An improved robust and sparse twin support vector regression via linear programming (2014)
  3. Demir, Begüm; Bruzzone, Lorenzo: A multiple criteria active learning method for support vector regression (2014)
  4. Dong, Yuan; Gao, Shan; Tao, Kun; Liu, Jiqing; Wang, Haila: Performance evaluation of early and late fusion methods for generic semantics indexing (2014)
  5. Chau, Asdrúbal López; Li, Xiaoou; Yu, Wen: Large data sets classification using convex-concave hull and support vector machine (2013)
  6. Srivastava, Ashok N.: Greener aviation with virtual sensors: a case study (2012)
  7. Vuppala, Anil Kumar; Rao, K.Sreenivasa; Chakrabarti, Saswat: Spotting and recognition of consonant-vowel units from continuous speech using accurate detection of vowel onset points (2012)
  8. Chen, Xiaobo; Yang, Jian; Ye, Qiaolin; Liang, Jun: Recursive projection twin support vector machine via within-class variance minimization (2011)
  9. Lughofer, Edwin; Trawiński, Bogdan; Trawiński, Krzysztof; Kempa, Olgierd; Lasota, Tadeusz: On employing fuzzy modeling algorithms for the valuation of residential premises (2011)
  10. Niu, Lingfeng: Parallel algorithm for training multiclass proximal support vector machines (2011)
  11. Niu, Lingfeng; Yuan, Ya-Xiang: A parallel decomposition algorithm for training multiclass kernel-based vector machines (2011)
  12. Woodsend, Kristian; Gondzio, Jacek: Exploiting separability in large-scale linear support vector machine training (2011)
  13. Guo, Gao; Zhang, Jiang-She; Zhang, Gai-Ying: A method to sparsify the solution of support vector regression (2010)
  14. Krishna Mohan, C.; Yegnanarayana, B.: Classification of sport videos using edge-based features and autoassociative neural network models (2010)
  15. Mordohai, Philippos; Medioni, Gérard: Dimensionality estimation, manifold learning and function approximation using tensor voting (2010)
  16. Shan, Songqing; Wang, G.Gary: Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions (2010)
  17. Guo, Gao; Zhang, Jiang-She; Zhang, Gai-Ying: A method to sparsify the solution of support vector regression (2009)
  18. Joachims, Thorsten; Finley, Thomas; Yu, Chun-Nam John: Cutting-plane training of structural SVMs (2009)
  19. Shi, Yong; Tian, Yingjie; Chen, Xiaojun; Zhang, Peng: Regularized multiple criteria linear programs for classification (2009)
  20. Zhao, Yongping; Sun, Jianguo: Recursive reduced least squares support vector regression (2009)

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