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Abstract: We propose a framework for classifier design based on discriminative densities for representation of the differences of the class-conditional distributions in a way that is optimal for classification. The densities are selected from a parametrized set by constrained maximization of some objective function which measures the average (bounded) difference, i.e. (Update)
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BibTeX entry: (Update)
@misc{ peter-discriminative,
author = "Peter Meinicke and Thorsten Twellmann and Helge Ritter",
title = "Discriminative Densities from Maximum Contrast Estimation",
url = "citeseer.ist.psu.edu/meinicke02discriminative.html" }
Citations (may not include all citations):2133 Pattern Classification and Scene Analysis (context) - Duda, Hart - 1973
1662 Neural Networks for Pattern Recognition (context) - Bishop - 1995
1291 The Nature of Statistical Learning Theory (context) - Vapnik - 1995
524 Support-vector networks - Cortes, Vapnik - 1995
490 Pattern Recognition and Neural Networks - Ripley - 1996
191 Fast training of support vector machines using sequential mi.. (context) - Platt - 1999
149 Multivariate Density Estimation (context) - Scott - 1992
113 Learning with Kernels (context) - Scholkopf, Smola - 2002
38 Improvements to platt's SMO algorithm for SVM classifier des.. - Keerthi, Shevade et al. - 1999
11 Classification on proximity data with lp--machines - Graepel, Herbrich et al. - 1999
3 Maximum contrast classifiers (context) - Meinicke, Twellmann et al. - 2002
3 Submitted to Machine Learning (context) - Ratsch, Onoda et al. - 1998
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