2003 | OriginalPaper | Buchkapitel
Comparison of Log-linear Models and Weighted Dissimilarity Measures
verfasst von : Daniel Keysers, Roberto Paredes, Enrique Vidal, Hermann Ney
Erschienen in: Pattern Recognition and Image Analysis
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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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.