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	<title>eng.kulanov.org.ua &#187; HMM</title>
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	<link>http://eng.kulanov.org.ua</link>
	<description>GRID Compiting, Grid in Ukraine, Workload performance, modelling and prediction</description>
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		<title>Improve speed up for forward-backward algorithm</title>
		<link>http://eng.kulanov.org.ua/archives/294</link>
		<comments>http://eng.kulanov.org.ua/archives/294#comments</comments>
		<pubDate>Fri, 29 Jul 2011 12:32:37 +0000</pubDate>
		<dc:creator>Sergey Kulanov</dc:creator>
				<category><![CDATA[Matlab]]></category>
		<category><![CDATA[Software]]></category>
		<category><![CDATA[freesoft]]></category>
		<category><![CDATA[GRID Simulation]]></category>
		<category><![CDATA[HMM]]></category>
		<category><![CDATA[howto]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Markov Chain]]></category>
		<category><![CDATA[Markov chain builder]]></category>
		<category><![CDATA[markovian]]></category>
		<category><![CDATA[Probability]]></category>
		<category><![CDATA[Science]]></category>

		<guid isPermaLink="false">http://eng.kulanov.org.ua/?p=294</guid>
		<description><![CDATA[In the previous post we were talking about repmat function in MEX format. We can reach further improvements if replace forward-backward procedure with C implementation (MEX format). The source code for the forward-backward in C can be found in PMTK which  is a collection of Matlab/Octave functions, written by Matt Dunham, Kevin Murphy. The results  [...]]]></description>
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		<title>Speeding up with repmat function (MEX version)</title>
		<link>http://eng.kulanov.org.ua/archives/285</link>
		<comments>http://eng.kulanov.org.ua/archives/285#comments</comments>
		<pubDate>Thu, 28 Jul 2011 19:39:44 +0000</pubDate>
		<dc:creator>Sergey Kulanov</dc:creator>
				<category><![CDATA[Matlab]]></category>
		<category><![CDATA[Software]]></category>
		<category><![CDATA[bootstrap]]></category>
		<category><![CDATA[GRID Simulation]]></category>
		<category><![CDATA[HMM]]></category>
		<category><![CDATA[howto]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Markov Chain]]></category>
		<category><![CDATA[Markov chain builder]]></category>
		<category><![CDATA[markovian]]></category>
		<category><![CDATA[MEX]]></category>
		<category><![CDATA[Model]]></category>
		<category><![CDATA[multiple arguments]]></category>
		<category><![CDATA[Probability]]></category>
		<category><![CDATA[Programming]]></category>
		<category><![CDATA[Science]]></category>
		<category><![CDATA[Statistics]]></category>
		<category><![CDATA[toolbox]]></category>
		<category><![CDATA[Viterbi]]></category>

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		<description><![CDATA[To run your matlab applications with repmat &#8220;functionality&#8221; faster, you can use MEX format of this function from Lightspeed matlab toolbox. With the same initial conditions you can see &#8220;productivity boosting&#8221;. Figure 1 &#8211; with default repmat function. Figure 2 &#8211; with MEX version of repmat function.]]></description>
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		<title>Viterbi algorithm Demo</title>
		<link>http://eng.kulanov.org.ua/archives/116</link>
		<comments>http://eng.kulanov.org.ua/archives/116#comments</comments>
		<pubDate>Sat, 11 Sep 2010 06:45:22 +0000</pubDate>
		<dc:creator>Sergey Kulanov</dc:creator>
				<category><![CDATA[Matlab]]></category>
		<category><![CDATA[Statistics]]></category>
		<category><![CDATA[HMM]]></category>
		<category><![CDATA[Viterbi]]></category>

		<guid isPermaLink="false">http://eng.kulanov.org.ua/?p=116</guid>
		<description><![CDATA[En excellent tutorial on Viterbi Algorithm was fount at http://www.telecom.tuc.gr/~ntsourak/demo_viterbi.htm]]></description>
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		<title>HMM tutorial</title>
		<link>http://eng.kulanov.org.ua/archives/75</link>
		<comments>http://eng.kulanov.org.ua/archives/75#comments</comments>
		<pubDate>Mon, 07 Dec 2009 10:56:38 +0000</pubDate>
		<dc:creator>Sergey Kulanov</dc:creator>
				<category><![CDATA[Matlab]]></category>
		<category><![CDATA[HMM]]></category>

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		<description><![CDATA[Very clear and good example of hidden markov model appliance presented in the tutorial (attached below) by Dr. Sung-JungCho HiddenMarkovModel Original materials one can find at http://isoft.postech.ac.kr/Course/CS704/LectureNotes/HiddenMarkovModel.pdf I check this tutorial with standard matlab functions and it seems working fine]]></description>
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