One-against-all-based multiclass svm strategies applied to vehicle plate character recognition

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2009, Pages 2153-2159

  Mota, Tiago C ^a   Thomé, Antonio Carlos G ^a  

^a FEDERAL UNIVERSITY OF RIO DE JANEIRO   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY CLASSIFIERS; MULTI-CLASSIFIER; MULTICLASS SVM; MULTICLASSIFIERS; ONE-AGAINST-ALL APPROACH; PERFORMANCE IMPROVEMENTS;

CHARACTER RECOGNITION; IMAGE PROCESSING;

NEURAL NETWORKS;

EID: 70449389394     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2009.5178902     Document Type: Conference Paper

References (18)

1. S. Haykin, Neural Networks: A Comprehensive Foundation. Upper Saddle River, NJ, USA: Prentice Hall PTR, 1994.
2. B. C. Guingo, R. J. Rodrigues, and A. C. G. Thome, "Automatic identification for automotives vehicles plates, " in 3rd WSEAS International Conference on Neural Networks and Applications (NNA '02), vol. 2, 2002, pp. 15-21.
3. C. J. C. Burges, "A tutorial on support vector machines for pattern recognition, " Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121-167, 1998.
4. C. Cortes and V. N. Vapnik, "Support-vector networks, " Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
5. D. M. Ushizima, A. C. Lorena, and A. C. P. L. F. de Carvalho, "Support vector machines applied to white blood cell recognition, " in Fifth International Conference on Hybrid Intelligent Systems (HIS'05), 2005, pp. 379-384.
6. B. E. Boser, I. M. Guyon, and V. N. Vapnik, "A training algorithm for optimal margin classifiers, " in Proceedings of the Fifth Annual Workshop on Computational Learning Theory. New York, NY, USA: ACM Press New York, 1992, pp. 144-152.
7. D. Luenberger, Linear and Nonlinear Programming. Reading, MA, USA: Addison-Wesley, 1984.
8. C.-W. Hsu and C.-J. Lin, "A comparison of methods for multiclass support vector machines, " IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 415-425, 2002.
9. J. C. Platt, N. Cristianini, and J. Shawe-taylor, "Large margin dags for multiclass classification, " in Advances in Neural Information Processing Systems. MIT Press, 2000, pp. 547-553.
10. E. L. Allwein, R. E. Schapire, and Y. Singer, "Reducing multiclass to binary: A unifying approach for margin classifiers, " The Journal of Machine Learning Research, vol. 1, pp. 113-141, 2001.
11. R. M. Rifkin and A. B. R. Klautau, "In defense of one-vs-all classification, " The Journal of Machine Learning Research, vol. 5, pp. 101-141, 2004.
12. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, PA, USA: Addison-Wesley, 1989.
13. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, 2nd ed. Berlin, Germany: Springer-Verlag, 1994.
14. I. T. Jolliffe, Principal Component Analysis. New York, NY, USA: Springer-Verlag, 1986.
15. S. Canu, Y. Grandvalet, and A. Rakotomamonjy, "SVM and kernel methods matlab toolbox, " Perception Systmes et Information, 2003.
16. B. Kijsirikul and N. Ussivakul, "Multiclass support vector machines using adaptive directed acyclic graph, " in Proceedings of the International Joint Conference on Neural Networks (IJCNN'02), 2002, pp. 980-985.
17. A. C. Lorena and A. C. P. L. F. de Carvalho, "Minimum spanning trees in hierarchical multiclass support vector machines generation, " in Proceedings of the 18th International Conference on Innovations in Applied Artificial Intelligence. Bari, Italy: Springer-Verlag, 2005, pp. 422-431.
18. A. Asuncion and D. J. Newman, "UCI machine learning repository, " 2007. [Online]. Available: http://www.ics.uci.edu/~mlearn/MLRepository. html