A Context-Sensitive Technique Based on Support Vector Machines for Image Classification

Author:

Bovolo Francesca,Bruzzone Lorenzo

Publisher

Springer Berlin Heidelberg

Reference6 articles.

1. Vapnik, V.N.: Statistical Learning Theory. Wiley, Chichester (1998)

2. Geman, S., Geman, D.: Stochastic Relaxation, Gibbs Distribution, and Bayesian Restoration of Images. IEEE Trans. Pattern Anal. Machine Intell. PAMI-6, 768–778 (1984)

3. Platt, J.C.: Probabilistic Outputs for Support Vector Machines and Comparison to Regularized Likelihood Methods. In: Smola, A., Bartlett, P., Scholkopf, B., Schuurmans, D. (eds.) Advances in Large Margin Classifiers. MIT Press, Cambridge (1999)

4. Besag, J.: On the Statistical Analysis of Dirty Pictures. J. Roy. Statist. Soc. B 48, 259–302 (1986)

5. Marroquin, J., Mitter, S., Poggio, T.: Probabilistic Solution of Ill-posed Problems in Computational Vision. J. Amer. Stat. Assoc. 82, 76–89 (1987)

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