Optimal artificial neural network architecture selection for bagging

T. Andersen, M. Rimer, T. Martinez

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations

Abstract

This paper studies the performance of standard architecture selection strategies, such as cost/performance and CV based strategies, for voting methods such as bagging. It is shown that standard architecture selection strategies are not optimal for voting methods and tend to underestimate the complexity of the optimal network architecture, since they only examine the performance of the network on an individual basis and do not consider the correlation between responses from multiple networks.

Original languageEnglish
Pages790-795
Number of pages6
StatePublished - 2001
EventInternational Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States
Duration: 15 Jul 200119 Jul 2001

Conference

ConferenceInternational Joint Conference on Neural Networks (IJCNN'01)
Country/TerritoryUnited States
CityWashington, DC
Period15/07/0119/07/01

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