BudgetedSVM: A toolbox for scalable SVM approximations

Journal of Machine Learning Research

Volumn 14, Issue , 2013, Pages 3813-3817

  Djuric, Nemanja ^a   Lan, Liang ^a   Vucetic, Slobodan ^a   Wang, Zhuang ^b  

^a TEMPLE UNIVERSITY   (United States)

^b IBM   (United States)

Author keywords

Large scale learning; Machine learning toolbox; Non linear classification; SVM

Indexed keywords

APPROXIMATORS; HIGH DIMENSIONAL DATA; HIGH-DIMENSIONAL; LARGE-SCALE LEARNING; NONLINEAR CLASSIFICATION; NONLINEAR PROBLEMS; STOCHASTIC GRADIENT DESCENT; SVM;

APPLICATION PROGRAMMING INTERFACES (API); BUDGET CONTROL;

SUPPORT VECTOR MACHINES;

EID: 84893493171     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (11)

1. C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Trans-actions on Intelligent Systems and Technology, 2:27:1-27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
2. K. Crammer and Y. Singer. On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research, 2:265-292, 2002.
3. C.-J. Hsieh, K.-W. Chang, C.-J. Lin, S. S. Keerthi, and S. Sundararajan. A dual coordinate descent method for large-scale linear SVM. In International Conference on Machine Learning, pages 408-415, 2008.
4. F. Lauer and Y. Guermeur. MSVMpack: A multi-class support vector machine package. Journal of Machine Learning Research, 12:2293-2296, 2011.
5. S. Shalev-Shwartz, Y. Singer, and N. Srebro. Pegasos: Primal estimated sub-gradient solver for SVM. In International Conference on Machine Learning, pages 807-814, 2007.
6. Z. Wang and S. Vucetic. Tighter perceptron with improved dual use of cached data for model representation and validation. In International Joint Conference on Neural Networks, pages 3297-3302, 2009.
7. Z. Wang and S. Vucetic. Online passive-aggressive algorithms on a budget. In International Conference on Artificial Intelligence and Statistics, pages 908-915, 2010.
8. Z. Wang, N. Djuric, K. Crammer, and S. Vucetic. Trading representability for scalability: Adaptive multi-hyperplane machine for nonlinear classification. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2011.
9. Z. Wang, K. Crammer, and S. Vucetic. Breaking the curse of kernelization: Budgeted stochastic gradient descent for large-scale SVM training. Journal of Machine Learning Research, 13:3103-3131, 2012.
10. K. Zhang, L. Lan, Z.Wang, and F. Moerchen. Scaling up kernel SVM on limited resources: A lowrank linearization approach. In International Conference on Artificial Intelligence and Statistics, 2012.
11. Z. A. Zhu, W. Chen, G. Wang, C. Zhu, and Z. Chen. P-packSVM: Parallel primal gradient descent kernel SVM. In IEEE International Conference on Data Mining, 2009.