A Generalized Class of Boosting Algorithms Based on Recursive Decoding Models

Authors:
Elizabeth Tapia;José Carlos González;Julio Villena
Affiliations:
-;-;-
Venue:
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Year:
2001

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Abstract

A communication model for the Hypothesis Boosting (HB) problem is proposed. Under this model, AdaBoost algorithm can be viewed as a threshold decoding approach for a repetition code. Generalization of such decoding view under theory of theory of Recursive Error Correcting Codes allows the formulation of a generalized class of low-complexity learning algorithms applicable in high dimensional classification problems. In this paper, an instance of this approach suitable for High Dimensional Features Spaces (HDFS) is presented.