Title
Improving boosting performance with a local combination of learners
Date Issued
01 December 2010
Access level
metadata only access
Resource Type
conference paper
Author(s)
Universidad Carlos III de Madrid
Publisher(s)
Institute of Electrical and Electronics Engineers Inc.
Abstract
This work explores the possibility of improving the performance of Real Adaboost ensemble classifiers by replacing their standard linear combination of learners by a gating scheme. This more powerful fusion method is defined following the epoch-by-epoch construction of boosting ensembles. Preliminary experimental results support the potential of this new approach. © 2010 IEEE.
Language
English
OCDE Knowledge area
Ingeniería de sistemas y comunicaciones
Ciencias de la información
Scopus EID
2-s2.0-79959436673
Resource of which it is part
Proceedings of the International Joint Conference on Neural Networks
ISBN of the container
978-142446917-8
Conference
2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
Sources of information:
Directorio de Producción Científica
Scopus