Abstract
We compare two successful discriminative classification algorithms on three databases from the UCI and STATLOG repositories. The two approaches are the log-linear model for the class posterior probabilities and class-dependent weighted dissimilarity measures for nearest neighbor classifiers. The experiments show that the maximum entropy based log-linear classifier performs better for the equivalent of a single prototype. On the other hand, using multiple prototypes the weighted dissimilarity measures outperforms the log-linear approach. This result suggests an extension of the log-linear method to multiple prototypes.
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Keysers, D., Paredes, R., Vidal, E., Ney, H. (2003). Comparison of Log-linear Models and Weighted Dissimilarity Measures. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_43
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DOI: https://doi.org/10.1007/978-3-540-44871-6_43
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