PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Comparison of Classifier Fusion Methods for Classification in Pattern Recognition Tasks.
F. Moreno-Seco, José Iñesta, P. Ponce de León and Luisa Mico
In: Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2006) and Statistical Techniques in Pattern Recognition (SPR 2006), 17-19 August 2006, Hong Kong.

Abstract

This work presents a comparison of current research in the use of voting ensembles of classifiers in order to improve the accuracy of single classifiers and make the performance more robust against the difficulties that each individual classifier may have. Also, a number of combination rules are proposed. Different voting schemes are discussed and compared in order to study the performance of the ensemble in each task. The ensembles have been trained on real data available for benchmarking and also applied to a case study related to statistical description models of melodies for music genre recognition.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:2526
Deposited By:Luisa Mico
Deposited On:22 November 2006