Feature Selection with Non-Parametric Mutual Information for Adaboost Learning

Authors:
Xavier Baró;Jordi Vitrià
Affiliations:
Centre de Visió per Computador;Centre de Visió per Computador and Dept. de Ciències de la Computació UAB, Edifici O-Campus UAB, 08193 Bellaterra, Barcelona, Catalonia, Spain, {xbaro,jordi}@cvc.uab.es
Venue:
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Year:
2005

Citing 1
Cited 1

Mutual Information in Learning Feature Transformations

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning

Real-time object detection using an evolutionary boosting strategy

Proceedings of the 2006 conference on Artificial Intelligence Research and Development

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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.