Difference between revisions of "Publications:Leverages Based Neural Networks Fusion"
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| − | |Name=Verikas, Antanas | + | |Name=Verikas, Antanas (av) (0000-0003-2185-8973) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938));Bacauskiene, Marija (Kaunas University of Technology, Lithuania);Gelzinis, Adas (Kaunas University of Technology, Lithuania) |
|Title=Leverages Based Neural Networks Fusion | |Title=Leverages Based Neural Networks Fusion | ||
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|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:300225}} | |diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:300225}} | ||
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Latest revision as of 21:40, 30 September 2016
| Title | Leverages Based Neural Networks Fusion |
|---|---|
| Author | Antanas Verikas and Marija Bacauskiene and Adas Gelzinis |
| Year | 2004 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | Neural information processing |
| Conference | 11th International Conference, ICONIP 2004, Calcutta |
| DOI | http://dx.doi.org/10.1007/978-3-540-30499-9_68 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:300225 |
| Abstract | To improve estimation results, outputs of multiple neural networks can be aggregated into a committee output. In this paper, we study the usefulness of the leverages based information for creating accurate neural network committees. Based on the approximate leave-one-out error and the suggested, generalization error based, diversity test, accurate and diverse networks are selected and fused into a committee using data dependent aggregation weights. Four data dependent aggregation schemes – based on local variance, covariance, Choquet integral, and the generalized Choquet integral – are investigated. The effectiveness of the approaches is tested on one artificial and three real world data sets. |
