A fast method for training linear SVM in the primal

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5211 LNAI, Issue PART 1, 2008, Pages 272-287

  Do, Trinh Minh Tri ^a   Artières, Thierry ^a  

^a UNIVERSITÉ PIERRE ET MARIE CURIE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS; ROBOT LEARNING; SUPPORT VECTOR MACHINES;

CUTTING PLANES; FAST ALGORITHMS; FAST METHODS; LINEAR SUPPORT VECTOR MACHINES; LINEAR SVM; NEW ALGORITHMS; STATE OF THE ARTS; SUBGRADIENT METHODS; TRAINING SAMPLES;

LEARNING SYSTEMS;

EID: 56049094624     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-87479-9_36     Document Type: Conference Paper

References (15)

1. Osuna, E., Freund, R., Girosi, F.: Training support vector machines: An application to face detection. In: CVPR 1997 (1997)
2. Platt, J.C.: Sequential minimal optimization: A fast algorithm for training support vector machines. Technical report, Microsoft Research (1998)
3. Joachims, T.: Making large-scale SVM learning practical. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge (1999)
4. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001)
5. Bordes, A., Ertekin, S., Weston, J., Bottou, L.: Fast kernel classifiers with online and active learning. Journal of Machine Learning Research 6, 1579-1619 (2005)
6. Tsang, I.W., Kwok, J.T., Cheung, P.M.: Core vector machines: Fast svm training on very large data sets. J. Mach. Learn. Res. 6, 363-392 (2005)
7. Chapelle, O.: Training a support vector machine in the primal. Neural Computation 19(5), 1155-1178 (2007)
8. Mangasarian, O.L., Musicant, D.R.: Lagrangian support vector machines. Journal of Machine Learning Research 1, 161 (2001)
9. Zhang, T.: Solving large scale linear prediction problems using stochastic gradient descent algorithms. In: ICML 2004, vol. 116. ACM Press, New York (2004)
10. Shalev-Shwartz, S., Singer, Y., Srebro, N.: Pegasos: Primal estimated sub-gradient solver for SVM. In: ICML 2007, pp. 807-814. ACM Press, New York (2007)
11. Joachims, T.: Training linear SVMs in linear time. In: ACM SIGKDD International Conference On Knowledge Discovery and Data Mining (KDD), pp. 217-226 (2006)
12. Teo, C.H., Le, Q.V., Smola, A., Vishwanathan, S.V.: A scalable modular convex solver for regularized risk minimization. In: KDD 2007, pp. 727-736 (2007)
13. Nedic, A., Bertsekas, D.: Convergence rate of incremental subgradient algorithms. In: Uryasev, S., Pardalos, P.M. (eds.) Stochastic Optimization: Algorithms and Applications. Kluwer Academic, Dordrecht (2000)
14. Tsochantaridis, I., Hofmann, T., Joachims, T., Altun, Y.: Support vector machine learning for interdependent and structured output spaces. In: ICML 2004, pp. 104-112 (2004)
15. Bertsekas, D.P., Nedic, A., Ozdaglar, A.E.: Convex analysis and optimization. Athena Scientific, Belmont (2003)