Supervised classification using MCMC methods

Author

Davy, Manuel ; Doncarli, Christian ; Tourneret, Jean-Yves

Author_Institution

IRCyN, Nantes, France

Volume

1

fYear

2000

fDate

2000

Firstpage

33

Abstract

This paper addresses the problem of supervised classification using general Bayesian learning. General Bayesian learning consists of estimating the unknown class-conditional densities from a set of labelled samples. However, the estimation requires to evaluate intractable multidimensional integrals. This paper studies an implementation of general Bayesian learning based on Markov chain Monte Carlo (MCMC) methods

Keywords

Bayes methods; Markov processes; Monte Carlo methods; learning (artificial intelligence); learning systems; parameter estimation; signal classification; Bayesian learning; MCMC methods; Markov chain Monte Carlo methods; intractable multidimensional integrals; labelled samples; supervised classification; unknown class-conditional densities; Bayesian methods; Bismuth; Chirp; Closed-form solution; Decision theory; Monte Carlo methods; Multidimensional signal processing; Multidimensional systems; Probability density function; Supervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on

Conference_Location

Istanbul

ISSN

1520-6149

Print_ISBN

0-7803-6293-4

Type

conf

DOI

10.1109/ICASSP.2000.861854

Filename

861854