Abstract
Non-binary classification has been usually addressed by training several binary classification when using Support Vector Machines (SVMs), because its performance does not degrade compared to the multi-class SVM and it is simpler to train and implement. In this paper we show that the binary classifiers in which the multi-classification relies are not independent from each other and using a puncturing mechanism this dependence can be pruned, obtaining much better multi-classification schemes as shown by the carried out experiments.
This work has been partially supported by CICYT grant TIC2000-0380-C03-03.
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References
E. Allwein, R. Schapire, and Y. Singer. Reducing multiclass to binary: A unifying approach for margin classifiers. Machine Learning Research, p. 113–141, 2000.
T. G. Dietterich and G. Bakiri. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2:263–286, 1995.
T. Hastie and R. Tibshirani. Classification by pairwise coupling. The annals of statistics, 26(2):451–471, 1998.
B. Schölkopf and A. Smola. Learning with kernels. M.I.T. Press, 2001.
B. Schölkopf, K.-K. Sung, C. J.C. Burges, F. Giros, P. Niyogi, T. Poggio, and V. N. Vapnik. Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans. on Signal Processing, 45(11):2758–2765, Nov. 1997.
J. Weston and C. Watkins. Multi-class support vector machines. Technical Report CSD-TR-98-04, Royal Holloway, University of London, UK, 1998.
S. B. Wicker. Error Control Systems. Prentice Hall, 1995.
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Pérez-Cruz, F., Artés-Rodríguez, A. (2002). Puncturing Multi-class Support Vector Machines. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_122
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DOI: https://doi.org/10.1007/3-540-46084-5_122
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