Comparison of Log-linear Models and Weighted Dissimilarity Measures

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.

• Type: Conference
• Year: 2003
• Where: IBPRIA
• Authors: Daniel Keysers, Roberto Paredes, Enrique Vidal, Hermann Ney
• Keywords: Class-dependent Weighted Dissimilarity, IBPRIA 2003, Image Analysis, Multiple Prototypes, Weighted Dissimilarity Measures