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Selecting variables for neural network committees
Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368, Kaunas, Lithuania.
Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368, Kaunas, Lithuania.
Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).ORCID-id: 0000-0003-2185-8973
2006 (Engelska)Ingår i: Advances in neural networks - ISNN 2006: third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006 ; proceedings. I / [ed] Jun Wang, Berlin: Springer Berlin/Heidelberg, 2006, s. 837-842Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

The aim of the variable selection is threefold: to reduce model complexity, to promote diversity of committee networks, and to find a trade-off between the accuracy and diversity of the networks. To achieve the goal, the steps of neural network training, aggregation, and elimination of irrelevant input variables are integrated based on the negative correlation learning [1] error function. Experimental tests performed on three real world problems have shown that statistically significant improvements in classification performance can be achieved from neural network committees trained according to the technique proposed.

Ort, förlag, år, upplaga, sidor
Berlin: Springer Berlin/Heidelberg, 2006. s. 837-842
Serie
Lecture Notes in Computer Science, ISSN 0302-9743 ; 3971
Nyckelord [en]
Neural Network Committees
Nationell ämneskategori
Teknik och teknologier
Identifikatorer
URN: urn:nbn:se:hh:diva-2001DOI: 10.1007/11759966_123ISI: 000238112000123Scopus ID: 2-s2.0-33745882620Lokalt ID: 2082/2396ISBN: 978-3-540-34439-1 OAI: oai:DiVA.org:hh-2001DiVA, id: diva2:239219
Konferens
third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006
Tillgänglig från: 2008-10-06 Skapad: 2008-10-06 Senast uppdaterad: 2014-11-10Bibliografiskt granskad

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