BCN Perceptual Computing Lab Repository

Feature Selection with Non-Parametric Mutual Information for Adaboost Learning

Baró, Xavier and Vitrià, Jordi (2005) Feature Selection with Non-Parametric Mutual Information for Adaboost Learning. Artificial Intelligence Resarch and Development, Frontiers in Artificial Intelligence and Applications, 131 . pp. 131-138.

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Abstract

This paper describes a feature selection method based on the quadratic mutual information. We describe the needed formulation to estimate the mutual information from the data. This paper is motivated for the high time cost of the training process using the classical boosting algorithms. This method allows to reuse part of the training time used in the first training process to speed up posterior training to update the detectors in front of samples changes.

Item Type:Article
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ID Code:6
Deposited By:Xavier Baró
Deposited On:15 May 2009 15:21
Last Modified:29 Nov 2010 17:52

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