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	<title>Anomalous Records &#187; Uncategorized</title>
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	<link>http://www.anomalousrecords.com</link>
	<description>experimental, abstract, surreal and totally anormal</description>
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		<title>Detecting Anomalous Records in Categorical Datasets</title>
		<link>http://www.anomalousrecords.com/2009/08/31/detecting-anomalous-records-in-categorical-datasets/</link>
		<comments>http://www.anomalousrecords.com/2009/08/31/detecting-anomalous-records-in-categorical-datasets/#comments</comments>
		<pubDate>Mon, 31 Aug 2009 18:36:56 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[Uncategorized]]></category>

		<guid isPermaLink="false">http://www.anomalousrecords.com/?p=16</guid>
		<description><![CDATA[Another kind of anomalous records ist presented here at videolectures.net. The problem of detecting anomalous records in categorical datasets is solved by creating a model of normal data, later comparing test records against it. A probabilistic approach builds a likelihood model from the training data. Based on the complete record likelihood the records are tested [...]]]></description>
			<content:encoded><![CDATA[<p>Another kind of anomalous records ist presented here at <a href="http://videolectures.net/kdd07_das_dar/">videolectures.net</a>. The problem of detecting anomalous records in categorical datasets is solved by creating a model of normal data, later comparing test records against it. A probabilistic approach builds a likelihood model from the training data. Based on the complete record likelihood the records are tested for anomalousness. It is shown that there is a better way of detecting anomalies than with classical bays net approaches.</p>
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		<title>test</title>
		<link>http://www.anomalousrecords.com/2009/08/28/test/</link>
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		<pubDate>Fri, 28 Aug 2009 19:53:19 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[Uncategorized]]></category>

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		<description><![CDATA[just a test article
]]></description>
			<content:encoded><![CDATA[<p>just a test article</p>
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