Optimized cutting plane algorithm for support vector machines

Proceedings of the 25th International Conference on Machine Learning

Volumn , Issue , 2008, Pages 320-327

  Franc, Vojtěch ^a   Sonnenburg, Soeren ^a  

^a FRAUNHOFER FIRST   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING ALGORITHMS; MACHINE LEARNING; GEARS; IMAGE RETRIEVAL; LEARNING SYSTEMS; MULTILAYER NEURAL NETWORKS; ROBOT LEARNING;

APPROXIMATIVE METHOD; COMPUTATIONAL EFFORT; CUTTING PLANE ALGORITHMS; EMPIRICAL EVALUATIONS; FASTER CONVERGENCE; LINEAR SUPPORT VECTOR MACHINES; STATE OF THE ART; TRAINING ALGORITHMS;

SUPPORT VECTOR MACHINES;

APPROXIMATIVE METHODS; COMPUTATIONAL EFFORTS; CUTTING PLANE ALGORITHMS; DATA SETS; EMPIRICAL EVALUATIONS; FASTER CONVERGENCES; PARALLELIZING; SAMPLE SIZES; STATE-OF-THE ARTS; STRING KERNELS; SVM SOLVERS; TRAINING ALGORITHMS;

EID: 56449101964     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390156.1390197     Document Type: Conference Paper

References (15)

1. Bottou, L., & Bousquet, O. (2008). The tradeoffs of large scale learning. In NIPS 20. MIT Press.
2. Chang, C.-C., & Lin, C.-J. (2001). LIBSVM: a library for svms. http://www.csie.ntu.edu.tw/~cjlin/libsvm.
3. Chapelle, O. (2007). Training a Support Vector Machine in the Primal. Neural Comp., 19, 1155-1178.
4. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273-297.
5. Cristianini, N., & Shwawe-Taylor, J. (2000). An introduction to support vector machines. Cambridge, UK: CUP.
6. Fan, R.-E., Chen, P.-H., & Lin, C.-J. (2005). Working set selection using second order information for training svm. Journal of Machine Learning Research, 6, 1889-1918.
7. Fawcett, T. (2003). Roc graphs: Notes and practical considerations for data mining researchers. Technical Report HPL-2003-4). HP Laboratories, Palo Alto, CA, USA.
8. Franc, V., & Sonnenburg, S. (2007). Optimized cutting plane algorithm for SVMs. Research Report: Electronic Publication 1). Fraunhofer Institute FIRST.
9. Joachims, T. (1999). Making large-scale SVM learning practical. Advances in Kernel Methods -Support Vector Learning (pp. 169-184). Cambridge, MA, USA: MIT Press.
10. Joachims, T. (2006). Training linear svms in linear time. KDD'06.
11. Lin, C.-J., Weng, R. C. & Keerthi, S. S. (2007). Trust region newton methods for large-scale logistic regression. ICML '07 (pp. 561 - 568). ACM New York.
12. Shwartz, S.-S., Singer, Y., & Srebro, N. (2007). Pegasos: Primal estimated sub-gradient solver for svm. ICML '07 (pp. 807-814). ACM Press.
13. Sindhwani, V., & Keerthi, S.-S. (2007). Newton methods for fast solution of semi-supervised linear svms. In Large scale kernel machines. MIT Press.
14. Teo, C. H., Le, Q., Smola, A., & Vishwanathan, S. (2007). A scalable modular convex solver for regularized risk minimization. KDD'07.
15. Zanni, L., Serafini, T., & Zanghirati, G. (2006). Parallel software for training. JMLR, 7, 1467-1492.