Abstract
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<p>To improve estimation results, ou … <p>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.</p> and three real world data sets.</p>
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Author
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Antanas Verikas +
, Marija Bacauskiene +
, Adas Gelzinis +
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Conference
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11th International Conference, ICONIP 2004, Calcutta
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DOI
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http://dx.doi.org/10.1007/978-3-540-30499-9_68 +
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Diva
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http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:300225
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EndPage
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451 +
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HostPublication
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Neural information processing +
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PublicationType
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Conference Paper +
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Series
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Lecture notes in computer science ; 3316 +
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StartPage
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446 +
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Title
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Leverages Based Neural Networks Fusion +
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Year
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2004 +
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Has queryThis property is a special property in this wiki.
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Publications:Leverages Based Neural Networks Fusion +
, Publications:Leverages Based Neural Networks Fusion +
, Publications:Leverages Based Neural Networks Fusion +
, Publications:Leverages Based Neural Networks Fusion +
, Publications:Leverages Based Neural Networks Fusion +
, Publications:Leverages Based Neural Networks Fusion +
, Publications:Leverages Based Neural Networks Fusion +
, Publications:Leverages Based Neural Networks Fusion +
, Publications:Leverages Based Neural Networks Fusion +
, Publications:Leverages Based Neural Networks Fusion +
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Categories |
Publication +
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Modification dateThis property is a special property in this wiki.
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30 September 2016 20:40:58 +
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