Relating Bayesian mixture-model classifiers to other popular pattern classifiers

Details Relating Bayesian mixture-model classifiers to other popular pattern classifiers Relating Bayesian mixture-model classifiers to other popular pattern classifiers

May 2001

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001aipc..568..120c&link_type=abstract

BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 20th International Workshop. AIP Conference Proceedi

Mathematics

Logic

Information Theory And Communication Theory, Neural Networks, Fuzzy Logic, Artificial Intelligence, Data Analysis: Algorithms And Implementation, Data Management

Scientific paper

This paper relates Bayesian mixture-model classifiers to other popular pattern classification algorithms including Parzen kernel, radial-basis-function neural network, and support vector machine algorithms. It compares both the training and operation modes of the different algorithms. It shows that the models underlying the other methods can be viewed as subsets of mixture models. In particular, it shows that support vector machine methods can be used to establish starting points for Bayesian mixture model training methods. .

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