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Abstract: The k-nearest neighbour (k-nn) model is a simple, popular classifier. Probabilistic k-nn is a more powerful variant in which the model is cast in a Bayesian framework using (reversible jump) Markov chain Monte Carlo methods to average out the uncertainy over the model parameters. (Update)
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BibTeX entry: (Update)
@misc{ everson-variable,
author = "Richard M. Everson and Jonathan E. Fieldsend",
title = "A Variable Metric Probabilistic k-Nearest-Neighbours Classifier",
url = "citeseer.ist.psu.edu/article/everson04variable.html" }
Citations (may not include all citations):696 UCI repository of machine learning databases (context) - Blake, Merz - 1998
326 Reversible jump Markov Chain Monte Carlo computation and Bay.. - Green - 1995
286 Nearest neighbor pattern classification (context) - Cover, Hart - 1967
126 Local polynomial modelling and its applications (context) - Fan, Gijbels - 1996
14 Markov Chain Monte Carlo Algorithms for the Bayesian analysi.. (context) - Larget, Simon - 1999
7 On input selection with reversible jump Markov chain Monte C.. - Sykacek - 2000 DBLP
6 A probabilistic nearest neighbour method for statistical pat.. (context) - Holmes, Adams - 2002
4 Bayesian Methods for Nonlinear Classification and Regression (context) - Denison, Holmes et al. - 2002
2 nearest-neighbour classfier for assessing consumer credit ri.. (context) - Henley, Hand - 1996
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