Sphere Support Vector Machines for large classification tasks

Neurocomputing

Volumn 101, Issue , 2013, Pages 59-67

  Strack, Robert ^a   Kecman, Vojislav ^a   Strack, Beata ^a   Li, Qi ^a  

^a Virginia Commonwealth University   (United States)

Author keywords

Classification; Core Vector Machines; Large datasets; Minimum enclosing ball; Support Vector Machines

Indexed keywords

CLASSIFICATION MODELS; CLASSIFICATION TASKS; CROSS VALIDATION; DATA SETS; FAST CLASSIFICATION; LARGE DATASETS; MINIMUM ENCLOSING BALL; NEAREST POINT; PARALLELIZATIONS; PROBABILISTIC TECHNIQUE; REAL DATA SETS; SPHERE SUPPORT VECTORS; STATE OF THE ART; SVM ALGORITHM; TRAINING PHASE; VECTOR MACHINES;

ALGORITHMS; BLENDING; CLASSIFICATION (OF INFORMATION);

SUPPORT VECTOR MACHINES;

ACCURACY; ARTICLE; CALCULATION; CLASSIFICATION ALGORITHM; DATA ANALYSIS; EXPERIMENTAL STUDY; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; PROBABILITY; SUPPORT VECTOR MACHINE;

EID: 84868618603     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.07.025     Document Type: Article

References (26)

1. T. Joachims, Making large-scale support vector machine learning practical, in: Advances in Kernel Methods, MIT Press, 1999, pp. 169-184.
2. Chang C.-H., Lin C.-J. LIBSVM. a library for support vector machines. ACM Trans. Intelligent Syst. Technol. 2011, 2:27:1-27:27.
3. Li Q., Salman R., Test E., Strack R., Kecman V. GPUSVM. a comprehensive CUDA based support vector machine package. Cent. Eur. J. Comput. Sci. 2011, 1:387-405.
4. Q. Li, Fast Parallel Machine Learning Algorithms for Large Datasets Using Graphic Processing Unit, Ph.D. Thesis, Virginia Commonwealth University, 2011.
5. Tsang I.W., Kwok J.T., Cheung P.-M. Core vector machines. fast SVM training on very large data sets. J. Mach. Learn. Res. 2005, 6:363-392.
6. K.P. Bennett, E.J. Bredensteiner, Duality and geometry in SVM classifiers, in: Proceedings of the 17th International Conference on Machine Learning, 2000, pp. 57-64.
7. I.W. Tsang, A. Kocsor, J.T. Kwok, Simpler core vector machines with enclosing balls, in: Proceedings of the 24th International Conference on Machine Learning-ICML'07, 2007, ACM Press, New York, USA, pp. 911-918.
8. J. C. Platt, Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines, in Advances in Kernel Method: Support Vector Learning, Schölkopf, Burges, and Smola, Eds. Cambridge, MA: MIT Press, 1998, pp. 185-208.
9. Cover T.M. Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Trans. Electron. Comput. 1965, 326-334.
10. Keerthi S.S., Shevade S.K., Bhattacharyya C., Murthy K.K. A fast iterative nearest point algorithm for support vector machine classifier design. IEEE Trans. Neural Networks 2000, 11:124-136.
11. Franc V., Hlaváč V. An iterative algorithm learning the maximal margin classifier. Pattern Recognition 2003, 36:1985-1996.
12. D.J. Crisp, C.J.C. Burges, A geometric interpretation of nu-SVM classifiers, in: Advances in Neural Information Processing Systems, vol. 12, 2000, pp. 223-229.
13. Mavroforakis M.E., Theodoridis S. A geometric approach to support vector machine (SVM) classification. IEEE Trans. Neural Networks 2006, 17:671-682.
14. I.W. Tsang, J.T. Kwok, P.-M. Cheung, Very large SVM training using core vector machines, in: Proceedings of the 10th International Workshop on Artificial Intelligence, 2005, pp. 349-356.
15. M. Badoiu, K.L. Clarkson, Smaller core-sets for balls, in: Proceedings of the 14th Annual ACM-SIAM Symposium on Discrete Algorithms, vol. 1, 2003, pp. 801-802.
16. A.J. Smola, B. Schölkopf, Sparse greedy matrix approximation for machine learning, in: Proceedings of the 17th International Conference on Machine Learning, 2000, pp. 911-918.
17. Tsang I.W., Kwok J.T., Zurada J.M. Generalized core vector machines. IEEE Trans. Neural Networks/A Publication of the IEEE Neural Networks Council 2006, 17:1126-1140.
18. S. Asharaf, M.N. Murty, S.K. Shevade, Multiclass core vector machine, in: Proceedings of the 24th International Conference on Machine Learning-ICML '07, 2007, pp. 41-48.
19. J. López, A. Barbero, J.R. Dorronsoro, An MDM solver for the nearest point problem in Scaled Convex Hulls, in: The 2010 International Joint Conference on Neural Networks (IJCNN), 2010, IEEE, pp. 1-8.
20. B.N. Kozinec, Recurrent algorithm separating convex hulls of two sets, in: Learning Algorithms in Pattern Recognition, 1973, pp. 43-50.
21. Michell B.F., Demyanov V.F., Malozemov V.N. Finding the point of polyhedron closest to the origin. SIAM J. Control 1974, 12:19-26.
22. Varma S., Simon R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics 2006, 7:91.
23. T. Scheffer, Error Estimation and Model Selection, Ph.D. Thesis, Technische Universität Berlin, 1999.
24. Loosli G., Canu S. Comments on the core vector machines. fast svm training on very large data sets. J. Mach. Learn. Res. 2007, 8:291-301.
25. I.W. Tsang, J.T. Kwok, Authors' Reply to the "Comments on the core vector machines: fast SVM training on very large data sets", 2007.
26. Agresti A., Coull B.A. Approximate is better than "exact" for interval estimation of binomial proportions. Am. Stat. 1998, 52:119-126.