A simplified multi-class support vector machine with reduced dual optimization

Pattern Recognition Letters

Volumn 33, Issue 1, 2012, Pages 71-82

  He, Xisheng ^a   Wang, Zhe ^b   Jin, Cheng ^a   Zheng, Yingbin ^a   Xue, Xiangyang ^a  

^a FUDAN UNIVERSITY   (China)

^b EAST CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Kernel based methods; Multi class classification; Pattern classification; Support vector machine

Indexed keywords

BINARY CLASSIFICATION; CLASSIFICATION ACCURACY; CLASSIFICATION ERRORS; CLASSIFICATION MODELS; DUAL PROBLEM; KERNEL BASED METHODS; MULTI-CLASS; MULTI-CLASS CLASSIFICATION; MULTI-CLASS SUPPORT VECTOR MACHINES; MULTICLASS SVM; NUMBER OF SAMPLES; SUPPORT VECTOR; TRAINING PROCESS;

COMPUTATIONAL COMPLEXITY; PATTERN RECOGNITION;

SUPPORT VECTOR MACHINES;

EID: 80054980558     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2011.09.035     Document Type: Article

References (42)

1. Asuncion, A.; Newman, D.; 2007. UCI machine learning repository. .
2. P. Baldi, and G. Pollastri A machine-learning strategy for protein analysis IEEE Intell. Systems 17 2 2002 28 35
3. P. Bartlett, M. Jordan, and J. McAuliffe Convexity, classification, and risk bounds J. Amer. Statist. Assoc. 101 2006 138 156 (Pubitemid 43500031)
4. E. Bredensteiner, and K. Bennett Multicategory classification by support vector machines Comput. Optim. Appl. 12 1999 53 79 (Pubitemid 129574925)
5. N.V. Chawla, N. Japkowicz, and A. Kolcz Editorial: Special issue on learning from imbalanced data sets ACM SIGKDD Explor. 6 1 2004 1 6
6. Cortes, C.; Vapnik, V.; 1995. Support vector networks. In: Machine Learning, pp. 273-297.
7. K. Crammer, and Y. Singer On the algorithmic implementation of multiclass kernel-based vector machines J. Machine Learn. Res. 2 2001 265 292
8. T. Dietterich, and G. Bakiri Solving multiclass learning problems via error-correcting output codes J. Artif. Intell. Res. 2 1995 263 286
9. P. Drineas, and M. Mahoney On the nyström method for approximating a gram matrix for improved kernel-based learning The J. Machine Learn. Res. 6 2005 2153 2175 (Pubitemid 41832630)
10. Fung, G.; Mangasarian, O.; 2001. Proximal support vector machine classifiers. In: Provost, F.; Srikant, R. (Eds.), Proc. KDD-2001: Knowledge Discovery and Data Mining, August 26-29, 2001, San Francisco, CA, Association for Computing Machinery, New York, pp. 77-86.
11. G. Fung, and O. Mangasarian Multicategory proximal support vector machine classifiers Machine Learn. 59 1-2 2005 77 97 (Pubitemid 40890413)
12. A. Ganapathiraju, J. Hamaker, and J. Picone Applications of support vector machines to speech recognition IEEE Trans. Signal Process. 52 8 2004 2348 2355
13. Y. Guermeur Combining discriminant models with new multi-class svms Pattern Anal. Appl. 5 2 2002 168 179 (Pubitemid 40830130)
14. C. Hsu, and C. Lin A comparison of methods for multi-class support vector machines IEEE Trans. Neural Netw. 13 2002 415 425 (Pubitemid 34475042)
15. C. Hsu, and C. Lin A simple decomposition method for support vector machines Machine Learn. 46 2002 291 314 (Pubitemid 34129973)
16. Hsu, C.; Lin, C.; 2008. Bsvm.
17. J. Hull A database for handwritten text recognition research IEEE Trans. Pattern Anal. Machine Intell. 16 5 1994 550 554
18. L. Khan, M. Awad, and B. Thuraisingham A new intrusion detection system using support vector machines and hierarchical clustering VLDB J. 16 4 2007 507 521
19. R. King, C. Feng, and A. Sutherland Statlog: Comparison of classification algorithms on large real-world problems Appl. Artif. Intell. 9 3 1995 289 333
20. S. Knerr, L. Personnaz, and G. Dreyfus Single-layer learning revisited: A stepwise procedure for building and training neural network J. Fogelman, Neurocomputing: Algorithms, Architectures and Applications 1990 Springer-Verlag
21. U. Kreßel Pairwise classification and support vector machines B. Schölkopf, C. Burges, A. Smola, Advances in Kernel Methods: Support Vector Learning 1999 MIT Press Cambridge, MA 255 268
22. S. Kumar, M. Mohri, and A. Talwalkar Ensemble Nyström method Neural Inform. Process. Systems 2009
23. Lee, Y.; Lin, Y.; Wahba, G. 2001. Multicategory Support Vector Machines. In: Wegman, E.; Braverman, A.; Goodman, A.; Smyth, P. (Eds.), Computing Science and Statistics, Vol. 33, pp. 498-512.
24. Y. Lee, Y. Lin, and G. Wahba Multicategory support vector machines: Theory and application to the classification of microarray data and satellite radiance data J. Amer. Statist. Assoc. 99 2004 67 81 (Pubitemid 38545195)
25. Li, M.; Kwok, J.T.; Liang Lu, B. 2010. Making large-scale Nyström approximation possible. In: Proc. 27th Internat. Conf. on Machine Learning.
26. Y. Liu Fisher consistency of multicategory support vector machines Eleventh Internat. Conf. Artif. Intell. Statist. 2007 289 296
27. O. Mangasarian, and D. Musicant Successive overrelaxation for support vector machines IEEE Trans. Neural Netw. 10 5 1999 1032 1037
28. O. Mangasarian, and D. Musicant Lagrangian support vector machines J. Machine Learn. Res. 1 2001 161 177 (Pubitemid 33687201)
29. Mohri, M.; Talwalkar, A.; 2009. Sampling techniques for the Nyström method. In: Twelfth Internat. Conf. on Artificial Intelligence and Statistics.
30. Mori, S.; Suen, C.; Yamamoto, K.; 1995. Historical Review of OCR Research and Development, pp. 244-273.
31. J. Platt, N. Cristianini, and J. Shawe-Taylor Large margin dags for multiclass classification Advances in Neural Information Processing Systems Vol. 12 2000 MIT Press 547 553
32. R. Rifkin, and A. Klautau In defense of one-vs-all classification J. Machine Learn. Res. 5 2004 101 141
33. J. Suykens, and J. Vandewalle Least squares support vector machine classifiers Neural Process. Lett. 9 3 1999 293 300
34. J. Suykens, and J. Vandewalle Multiclass least squares support vector machines Proc. Internat. Joint Conf. on Neural Networks (IJCNN99) 1999 World Scientific
35. Szedmak, S.; Shawe-Taylor, J.; Saunders, C.; Hardoon, D.; 2004. Multiclass classification by l1 norm support vector machine. In: Pattern Recognition and Machine Learning in Computer Vision Workshop.
36. Y. Tang, Y. Zhang, N. Chawla, and S. Krasser Svms modeling for highly imbalanced classification IEEE Trans. Systems Man Cybernet.; Part B 39 1 2009 281 288
37. V. Vapnik Statistical Learning Theory 1998 Wiley New York, NY
38. L. Wang, and X. Shen On l1-norm multiclass support vector machines: Methodology and theory J. Amer. Statist. Assoc. 102 2007 583 594
39. Weston, J.; Watkins, C.; 1999. Multi-class support vector machines. In: Proc. ESANN99.
40. Williams, C.; Seeger, M.; 2001. Using the Nyström Method to Speed Up Kernel Machines. In: Advances in Neural Information Processing Systems 13.
41. Xia, X.; Li, K.; 2008. A sparse multi-class least-squares support vector machine. In: IEEE Internat. Symposium on Industrial Electronics, 2008, ISIE 2008, pp. 1230-1235.
42. K. Zhang, I. Tsang, and J. Kwok Improved Nyström low-rank approximation and error analysis Proc. 25th Internat. Conf. Machine Learning 2008 ACM 1232 1239