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	<title>brain of matpalm &#187; naive bayes</title>
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	<link>http://matpalm.com/blog</link>
	<description>thoughts from a data scientist wannabe</description>
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		<title>semi supervised naive bayes for text classification</title>
		<link>http://matpalm.com/blog/2010/02/14/semi-supervised-naive-bayes-for-text-classification/</link>
		<comments>http://matpalm.com/blog/2010/02/14/semi-supervised-naive-bayes-for-text-classification/#comments</comments>
		<pubDate>Sun, 14 Feb 2010 11:46:42 +0000</pubDate>
		<dc:creator>matpalm</dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[e13]]></category>
		<category><![CDATA[naive bayes]]></category>
		<category><![CDATA[semi supervised]]></category>

		<guid isPermaLink="false">http://matpalm.com/blog/?p=299</guid>
		<description><![CDATA[experiment 13; a test of semi supervised naive bayes for text classification is complete.
semi supervised algorithms seem to work pretty well and i can see how they are a huge benefit for text classification where you can have an enormous corpus but not enough time to label it all&#8230;
]]></description>
			<content:encoded><![CDATA[<p>experiment 13; <a href="http://matpalm.com/semi_supervised_naive_bayes/">a test of semi supervised naive bayes for text classification</a> is complete.</p>
<p>semi supervised algorithms seem to work pretty well and i can see how they are a huge benefit for text classification where you can have an enormous corpus but not enough time to label it all&#8230;</p>
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		<title>an intro to semi supervised document classification</title>
		<link>http://matpalm.com/blog/2010/01/31/an-intro-to-semi-supervised-document-classification/</link>
		<comments>http://matpalm.com/blog/2010/01/31/an-intro-to-semi-supervised-document-classification/#comments</comments>
		<pubDate>Sun, 31 Jan 2010 04:02:33 +0000</pubDate>
		<dc:creator>matpalm</dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[naive bayes]]></category>
		<category><![CDATA[semi supervised]]></category>

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		<description><![CDATA[here&#8217;s a great lecture from tom mitchell about document classification using a semi supervised version of naive bayes.
semi supervised algorithms only require some of the training examples to be labeled and are able to make use of any unlabelled ones, very common when we have a huge corpus.
i&#8217;ve started an experiment brewing to test this [...]]]></description>
			<content:encoded><![CDATA[<p>here&#8217;s a great <a href="http://videolectures.net/mlas06_mitchell_sla/">lecture</a> from <a href="http://www.cs.cmu.edu/~tom/">tom mitchell</a> about document classification using a semi supervised version of naive bayes.</p>
<p>semi supervised algorithms only require some of the training examples to be labeled and are able to make use of any unlabelled ones, very common when we have a huge corpus.</p>
<p>i&#8217;ve started an experiment brewing to test this out by porting some <a href="http://matpalm.com/rss.feed/p3/">previous naive bayes work</a> i did to use this semi supervised scheme and will published it when it&#8217;s done.</p>
<p>cool stuff!!</p>
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