SoftDoubleMinOver: A simple procedure for maximum margin classification

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

Volumn 3697 LNCS, Issue , 2005, Pages 301-306

  Martinetz, Thomas ^a   Labusch, Kai ^a   Schneegaß, Daniel ^a  

^a UNIVERSITY OF LÜBECK   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; CLASSIFICATION (OF INFORMATION); COMPUTATION THEORY;

KERNELS; MINOVER ALGORITHM; SOFT MARGINS; SOFTDOUBLEMINOVER ALGORITHM;

ALGORITHMS;

EID: 33646229306     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (13)

1. Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20 (1995) 273-297
2. Vapnik, V.: The Nature of Statistical Learning Theory. Springer-Verlag, New York (1995)
3. LeCun, Y., Jackel, L., Bottou, L., Brunot, A., Cortes, C., Denker, J., Drucker, H., Guyon, I., Muller, U., Sackinger, E., Simard, P., Vapnik, V: Comparison of learning algorithms for handwritten digit recognition. Int.Conf.on Artificial Neural Networks (1995) 53-60
4. Osuna, E., Freund, R., Girosi, F.: Training support vector machines:an application to face detection. CVPR'97 (1997) 130-136
5. Schölkopf, B.: Support vector learning (1997)
6. Friess, T., Cristianini, N., Campbell, C.: The kernel adatron algorithm: a fast and simple learning procedure for support vector machine. Proc. 15th International Conference on Machine Learning (1998)
7. Platt, J.: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In: Advances in Kernel Methods - Support Vector Learning. MIT Press (1999) 185-208
8. Keerthi, S.S., Shevade, S.K., Bhattacharyya, C., Murthy, K.R.K.: A fast iterative nearest point algorithm for support vector machine classifier design. IEEE-NN11 (2000) 124-136
9. Li, Y., Long, P.: The relaxed online maximum margin algorithm. Machine Learning 46(1-3) (2002) 361-387
10. Krauth, W., Mezard, M.: Learning algorithms with optimal stability in neural networks. J.Phys.A 20 (1987) 745-752
11. Martinetz, T.: Minover revisited for incremental support-vector- classification. Lecture Notes in Computer Science 3175 (2004) 187-194
12. Cristianini, N., Shawe-Taylor, J.: Support Vector Machines (and other kernel-based learning methods). Cambridge University Press, Cambridge (2000)
13. King, R., Feng, C., Shutherland, A.: Statlog: comparison of classification algorithms on large real-world problems. Applied Artificial Intelligence 9 (1995) 259-287