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	<title>Publications:Selecting features with neural networks - Revision history</title>
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		<id>https://mw.hh.se/caisr/index.php?title=Publications:Selecting_features_with_neural_networks&amp;diff=3107&amp;oldid=prev</id>
		<title>Slawek at 20:41, 30 September 2016</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Publications:Selecting_features_with_neural_networks&amp;diff=3107&amp;oldid=prev"/>
		<updated>2016-09-30T20:41:28Z</updated>

		<summary type="html">&lt;p&gt;&lt;/p&gt;
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				&lt;td colspan=&quot;2&quot; style=&quot;background-color: #fff; color: #202122; text-align: center;&quot;&gt;← Older revision&lt;/td&gt;
				&lt;td colspan=&quot;2&quot; style=&quot;background-color: #fff; color: #202122; text-align: center;&quot;&gt;Revision as of 20:41, 30 September 2016&lt;/td&gt;
				&lt;/tr&gt;&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot; id=&quot;mw-diff-left-l4&quot; &gt;Line 4:&lt;/td&gt;
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&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot; id=&quot;mw-diff-left-l55&quot; &gt;Line 55:&lt;/td&gt;
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		<author><name>Slawek</name></author>
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		<id>https://mw.hh.se/caisr/index.php?title=Publications:Selecting_features_with_neural_networks&amp;diff=1104&amp;oldid=prev</id>
		<title>SlawekBot: 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=545121 |Name=Ve...&quot;</title>
		<link rel="alternate" type="text/html" href="https://mw.hh.se/caisr/index.php?title=Publications:Selecting_features_with_neural_networks&amp;diff=1104&amp;oldid=prev"/>
		<updated>2014-03-13T11:49:34Z</updated>

		<summary type="html">&lt;p&gt;Created page with &amp;quot;&amp;lt;div style=&amp;#039;display: none&amp;#039;&amp;gt; == Do not edit this section == &amp;lt;/div&amp;gt; {{PublicationSetupTemplate|Author=Antanas Verikas, Marija Bacauskiene, Kerstin Malmqvist |PID=545121 |Name=Ve...&amp;quot;&lt;/p&gt;
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|Name=Verikas, Antanas [av] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938]);Bacauskiene, Marija (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938]);Malmqvist, Kerstin (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938])&lt;br /&gt;
|Title=Selecting features with neural networks&lt;br /&gt;
|PublicationType=Conference Paper&lt;br /&gt;
|ContentType=Refereegranskat&lt;br /&gt;
|Language=eng&lt;br /&gt;
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|Status=&lt;br /&gt;
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|HostPublication=Neural Information Precessing : ICONIP-2001 proceedings&lt;br /&gt;
|Conference=8th International Conference on Neural Information Processing (ICONIP 2001), SHANGHAI, PEOPLES R CHINA, NOV 14-18, 2001&lt;br /&gt;
|StartPage=63&lt;br /&gt;
|EndPage=68&lt;br /&gt;
|Year=2001&lt;br /&gt;
|Edition=&lt;br /&gt;
|Pages=&lt;br /&gt;
|City=Shanghai&lt;br /&gt;
|Publisher=Fudan University Press&lt;br /&gt;
|Series=&lt;br /&gt;
|SeriesISSN=&lt;br /&gt;
|ISBN=7-309-03012-5&lt;br /&gt;
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|ISI=000176058200017&lt;br /&gt;
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|NBN=urn:nbn:se:hh:diva-18773&lt;br /&gt;
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|Keywords=Computer Science;Artificial Intelligence;Computer Science;Hardware &amp;amp; Architecture;Robotics&lt;br /&gt;
|Categories=Teknik och teknologier (2);Robotteknik och automation (20201)&lt;br /&gt;
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|Projects=&lt;br /&gt;
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|Abstract=&amp;lt;p&amp;gt;We present a neural network based approach for identifying salient features for classification in feed-forward neural networks. 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 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 five other feature selection methods, each of which banks on different concept. The algorithm developed outperformed the other methods by achieving a higher classification accuracy on all the problems tested.&amp;lt;/p&amp;gt;&lt;br /&gt;
|Opponents=&lt;br /&gt;
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|CreatedDate=2012-06-25&lt;br /&gt;
|PublicationDate=2012-08-17&lt;br /&gt;
|LastUpdated=2012-08-17&lt;br /&gt;
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