PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning iteratively a classifier with the Bayesian Model Averaging Principlestar
Rudy Sicard, Thierry Artières and Eric Petit
Pattern Recognition Volume 41, Number 3, pp. 930-938, 2008.

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Abstract

We present a learning algorithm for nominal vector data. It builds a complex classifier by adding iteratively a simple function that modifies the current classifier. In order to limit overtraining problem we focus on a class of such functions for which optimal Bayesian learning is tractable. We investigate a few classes of functions that yield to models that are similar to Naı¨ve Bayes and logistic classification. We report experimental results for a collection of standard data sets that show that our learning algorithm outperforms standard learning of such these standard models.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3672
Deposited By:Thierry Artieres
Deposited On:14 February 2008

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