https://mw.hh.se/caisr/index.php?action=history&feed=atom&title=Publications%3ASelecting_features_for_neural_network_committees 2026-04-04T17:58:29Z Revision history for this page on the wiki MediaWiki 1.35.13 https://mw.hh.se/caisr/index.php?title=Publications:Selecting_features_for_neural_network_committees&diff=3996&oldid=prev 2018-10-09T20:22:28Z

<p>Created page with &quot;&lt;div style=&#039;display: none&#039;&gt; == Do not edit this section == &lt;/div&gt; {{PublicationSetupTemplate|Author=Antanas Verikas, Marija Bacauskiene, Kerstin Malmqvist |PID=1254348 |Name=V...&quot;</p> <p><b>New page</b></p><div>&lt;div style=&#039;display: none&#039;&gt;<br /> == Do not edit this section ==<br /> &lt;/div&gt;<br /> {{PublicationSetupTemplate|Author=Antanas Verikas, Marija Bacauskiene, Kerstin Malmqvist<br /> |PID=1254348<br /> |Name=Verikas, Antanas (av) (0000-0003-2185-8973) (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650));Bacauskiene, Marija (Kaunas University of Technology, Department of Electric Power Systems, Kaunas, Lithuania);Malmqvist, Kerstin (kermalx) (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650))<br /> |Title=Selecting features for neural network committees<br /> |PublicationType=Conference Paper<br /> |ContentType=Refereegranskat<br /> |Language=eng<br /> |Journal=<br /> |JournalISSN=<br /> |Status=<br /> |Volume=<br /> |Issue=<br /> |HostPublication=Proceedings of the International Joint Conference on Neural Networks<br /> |Conference=International Joint Conference on Neural Networks (IJCNN &#039;02), Honolulu, HI, United States, 12-17 May, 2002<br /> |StartPage=215<br /> |EndPage=220<br /> |Year=2002<br /> |Edition=<br /> |Pages=<br /> |City=Piscataway<br /> |Publisher=IEEE<br /> |Series=<br /> |SeriesISSN=<br /> |ISBN=0-7803-7278-6<br /> |Urls=<br /> |ISRN=<br /> |DOI=http://dx.doi.org/10.1109/IJCNN.2002.1005472<br /> |ISI=000177402800040<br /> |PMID=<br /> |ScopusId=2-s2.0-0036076592<br /> |NBN=urn:nbn:se:hh:diva-38119<br /> |LocalId=<br /> |ArchiveNumber=<br /> |Keywords=<br /> |Categories=Telekommunikation (20204)<br /> |ResearchSubjects=<br /> |Projects=<br /> |Notes=<br /> |Abstract=&lt;p&gt;We present a neural network based approach for identifying salient features for classification in neural network committees. Our approach involves neural network training with an augmented cross-entropy error function. The augmented error function forces the neural network to keep low derivatives of the transfer functions of neurons of the network when learning a classification task. Such an approach reduces output sensitivity to the input changes. Feature selection is based on the reaction of the cross-validation data set classification error due to the removal of the individual features. We compared the approach with two other neural network based feature selection methods. The algorithm developed outperformed the methods by achieving a higher classification accuracy on three real world problems tested. ©2002 IEEE&lt;/p&gt;<br /> |Opponents=<br /> |Supervisors=<br /> |Examiners=<br /> |Patent=<br /> |ThesisLevel=<br /> |Credits=<br /> |Programme=<br /> |Subject=<br /> |Uppsok=<br /> |DefencePlace=<br /> |DefenceLanguage=<br /> |DefenceDate=<br /> |CreatedDate=2018-10-09<br /> |PublicationDate=2018-10-09<br /> |LastUpdated=2018-10-09<br /> |diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&amp;pid=diva2:1254348}}<br /> &lt;div style=&#039;display: none&#039;&gt;<br /> <br /> == Keep all hand-made modifications below ==<br /> &lt;/div&gt;{{PublicationDisplayTemplate}}</div>

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