Advertisement
Published in:
2007 | OriginalPaper | Chapter
Averaged Conservative Boosting: Introducing a New Method to Build Ensembles of Neural Networks
Authors : Joaquín Torres-Sospedra, Carlos Hernández-Espinosa, Mercedes Fernández-Redondo
Published in: Artificial Neural Networks – ICANN 2007
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
In this paper, a new algorithm called
Averaged Conservative Boosting
(
ACB
) is presented to build ensembles of neural networks. In
ACB
we mix the improvements that
Averaged Boosting
(
Aveboost
) and
Conservative Boosting
(
Conserboost
) made to
Adaptive Boosting
(
Adaboost
). In the algorithm we propose we have applied the conservative equation used in
Conserboost
along with the averaged procedure used in
Aveboost
in order to update the sampling distribution used in the training of
Adaboost
. We have tested the methods with seven databases from the
UCI repository
. The results show that the best results are provided by our method,
Averaged Conservative Boosting
.