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	<title>De Rerum Natura &#187; Machine Learning</title>
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	<link>http://www.corrada.com/blog</link>
	<description>Randomness, entropy, pattern matching, maps, geometry, knots, and scientific readings</description>
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		<title>The unsupervised inference problem for seven pairwise correlated recognizers is solvable</title>
		<link>http://www.corrada.com/blog/2011/08/21/the-unsupervised-inference-problem-for-seven-pairwise-correlated-recognizers-is-solvable/</link>
		<comments>http://www.corrada.com/blog/2011/08/21/the-unsupervised-inference-problem-for-seven-pairwise-correlated-recognizers-is-solvable/#comments</comments>
		<pubDate>Sun, 21 Aug 2011 12:10:09 +0000</pubDate>
		<dc:creator>Andrés</dc:creator>
				<category><![CDATA[Error Theory]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Unsupervised inference]]></category>

		<guid isPermaLink="false">http://www.corrada.com/blog/2011/08/21/the-unsupervised-inference-problem-for-seven-pairwise-correlated-recognizers-is-solvable/</guid>
		<description><![CDATA[It takes 127 variables to describe the space of all possible pair wise correlations between 7 recognizers. This is equal to the number of label voting patterns $L^R &#8211; 1 = 2^7 &#8211; 1 = 127$. Conjecture: the problem is solvable.]]></description>
		<wfw:commentRss>http://www.corrada.com/blog/2011/08/21/the-unsupervised-inference-problem-for-seven-pairwise-correlated-recognizers-is-solvable/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Unsupervised Inference</title>
		<link>http://www.corrada.com/blog/2010/12/07/unsupervised-inference/</link>
		<comments>http://www.corrada.com/blog/2010/12/07/unsupervised-inference/#comments</comments>
		<pubDate>Tue, 07 Dec 2010 22:13:52 +0000</pubDate>
		<dc:creator>Andrés</dc:creator>
				<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Scientific Readings]]></category>

		<guid isPermaLink="false">http://www.corrada.com/blog/?p=149</guid>
		<description><![CDATA[I am now one of the principals in Data Engines Corporation. The blog postings related to my research work will now appear mostly there with occasional cultural and philosophical musings related to them here. This blog will continue as the site for personal reflections on my readings. The big news for us is that we [...]]]></description>
		<wfw:commentRss>http://www.corrada.com/blog/2010/12/07/unsupervised-inference/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Robust voting in uncertain environments</title>
		<link>http://www.corrada.com/blog/2008/11/17/robust-voting-in-uncertain-environments/</link>
		<comments>http://www.corrada.com/blog/2008/11/17/robust-voting-in-uncertain-environments/#comments</comments>
		<pubDate>Mon, 17 Nov 2008 05:00:15 +0000</pubDate>
		<dc:creator>Andrés</dc:creator>
				<category><![CDATA[Compressed Sensing]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[precision error application]]></category>
		<category><![CDATA[Randomness]]></category>

		<guid isPermaLink="false">http://www.corrada.com/blog/?p=83</guid>
		<description><![CDATA[Combining the judgments of different recognizers is always better than using the best one alone. This observation is universal in machine learning realms. But it seldom gets used in practice. Why? For one, it costs more to implement. Instead of one recognizer, you must deploy several. Computing cycles grow linearly with the number of recognizers [...]]]></description>
		<wfw:commentRss>http://www.corrada.com/blog/2008/11/17/robust-voting-in-uncertain-environments/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Asymmetry in likelihood of causing the error</title>
		<link>http://www.corrada.com/blog/2008/03/31/asymmetry-in-likelihood-of-causing-the-error/</link>
		<comments>http://www.corrada.com/blog/2008/03/31/asymmetry-in-likelihood-of-causing-the-error/#comments</comments>
		<pubDate>Mon, 31 Mar 2008 09:12:08 +0000</pubDate>
		<dc:creator>Andrés</dc:creator>
				<category><![CDATA[Error Theory]]></category>
		<category><![CDATA[Machine Learning]]></category>

		<guid isPermaLink="false">http://www.corrada.com/blog/?p=62</guid>
		<description><![CDATA[As the number of models increase, the observed pattern in prediction discrepancies allows one to decide what is causing it assuming uniform uncertainty among all possible scenarios. The observed error pattern will be consistent with many different scenarios. In some scenarios, the noisy model predictions are due to the model being correct and the other [...]]]></description>
		<wfw:commentRss>http://www.corrada.com/blog/2008/03/31/asymmetry-in-likelihood-of-causing-the-error/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Variations in student responses to a multiple exam for latent group discovery</title>
		<link>http://www.corrada.com/blog/2008/03/13/variations-in-student-responses-to-a-multiple-exam-for-latent-group-discovery/</link>
		<comments>http://www.corrada.com/blog/2008/03/13/variations-in-student-responses-to-a-multiple-exam-for-latent-group-discovery/#comments</comments>
		<pubDate>Fri, 14 Mar 2008 00:03:37 +0000</pubDate>
		<dc:creator>Andrés</dc:creator>
				<category><![CDATA[Error Theory]]></category>
		<category><![CDATA[Fourier Analysis]]></category>
		<category><![CDATA[Group Theory]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[latent labels]]></category>

		<guid isPermaLink="false">http://www.corrada.com/blog/2008/03/13/variations-in-student-responses-to-a-multiple-exam-for-latent-group-discovery/</guid>
		<description><![CDATA[Questions in an exam are detectors of student competency. Students are detectors of the correct answers in a test. What is the variation in the student&#8217;s model of the correct exam? The precision error equations can be used to construct a covariance matrix for the students instead of the questions. What makes the difference is [...]]]></description>
		<wfw:commentRss>http://www.corrada.com/blog/2008/03/13/variations-in-student-responses-to-a-multiple-exam-for-latent-group-discovery/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>The number of clusters problem as precision error minimization</title>
		<link>http://www.corrada.com/blog/2008/03/05/the-number-of-clusters-problem-as-precision-error-minimization/</link>
		<comments>http://www.corrada.com/blog/2008/03/05/the-number-of-clusters-problem-as-precision-error-minimization/#comments</comments>
		<pubDate>Wed, 05 Mar 2008 14:45:56 +0000</pubDate>
		<dc:creator>Andrés</dc:creator>
				<category><![CDATA[Error Theory]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[number of clusters problem]]></category>
		<category><![CDATA[precision error]]></category>

		<guid isPermaLink="false">http://www.corrada.com/blog/2008/03/05/the-number-of-clusters-problem-as-precision-error-minimization/</guid>
		<description><![CDATA[One possible application of the precision error tensors framework is to use it as a criterion for selecting the number of clusters needed to describe a dataset. The number of clusters problem refers to the generic problem of deciding how many clusters describe a dataset. Many clustering algorithms exist. Deciding which one is appropriate in [...]]]></description>
		<wfw:commentRss>http://www.corrada.com/blog/2008/03/05/the-number-of-clusters-problem-as-precision-error-minimization/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Precision error tensors</title>
		<link>http://www.corrada.com/blog/2008/03/01/precision-error-tensors/</link>
		<comments>http://www.corrada.com/blog/2008/03/01/precision-error-tensors/#comments</comments>
		<pubDate>Sat, 01 Mar 2008 19:06:52 +0000</pubDate>
		<dc:creator>Andrés</dc:creator>
				<category><![CDATA[Error Theory]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[covariance matrix]]></category>
		<category><![CDATA[precision error]]></category>
		<category><![CDATA[tensors]]></category>

		<guid isPermaLink="false">http://www.corrada.com/blog/2008/03/01/precision-error-tensors/</guid>
		<description><![CDATA[Mathematical objects have dimensions associated with them. The temperature outside my house is measured as a single number or scalar. It is a one-dimensional quantity. This fact can be observed in how mercury thermometers are built: they are a long tube or line. Thermometers are never built as squares. The position of house in a [...]]]></description>
		<wfw:commentRss>http://www.corrada.com/blog/2008/03/01/precision-error-tensors/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Precision error covariance matrix for a Physics multiple-choice exam</title>
		<link>http://www.corrada.com/blog/2008/02/29/precision-error-covariance-matrix-for-a-physics-multiple-choice-exam/</link>
		<comments>http://www.corrada.com/blog/2008/02/29/precision-error-covariance-matrix-for-a-physics-multiple-choice-exam/#comments</comments>
		<pubDate>Fri, 29 Feb 2008 20:57:59 +0000</pubDate>
		<dc:creator>Andrés</dc:creator>
				<category><![CDATA[Error Theory]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Randomness]]></category>
		<category><![CDATA[precision error covariance matrix]]></category>

		<guid isPermaLink="false">http://www.corrada.com/blog/2008/02/29/precision-error-covariance-matrix-for-a-physics-multiple-choice-exam/</guid>
		<description><![CDATA[I have applied the autonomous difference equations to test the quality of ten out of twenty questions I used in a Physics exam I gave in the Spring of 2006 to an introductory class for engineering students. That dark square in position six of the matrix corresponds to the question least likely to be answered [...]]]></description>
		<wfw:commentRss>http://www.corrada.com/blog/2008/02/29/precision-error-covariance-matrix-for-a-physics-multiple-choice-exam/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>To err is human, to study your errors is glorious</title>
		<link>http://www.corrada.com/blog/2008/02/17/to-err-is-human-to-study-your-errors-is-glorious/</link>
		<comments>http://www.corrada.com/blog/2008/02/17/to-err-is-human-to-study-your-errors-is-glorious/#comments</comments>
		<pubDate>Mon, 18 Feb 2008 01:59:33 +0000</pubDate>
		<dc:creator>Andrés</dc:creator>
				<category><![CDATA[Error Theory]]></category>
		<category><![CDATA[Fourier Analysis]]></category>
		<category><![CDATA[Group Theory]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Maps]]></category>
		<category><![CDATA[Randomness]]></category>
		<category><![CDATA[error estimation]]></category>
		<category><![CDATA[model precision]]></category>

		<guid isPermaLink="false">http://www.corrada.com/blog/2008/02/17/to-err-is-human-to-study-your-errors-is-glorious/</guid>
		<description><![CDATA[I&#8217;ve been sick all week but today has been the worst. In between my sleeping hallucinations I have been thinking a lot about a proposal I&#8217;m currently writing on the use of non-commutative harmonic analysis to study mapping error patterns. It has become clear that the approach we are advocating at the AIRS lab is [...]]]></description>
		<wfw:commentRss>http://www.corrada.com/blog/2008/02/17/to-err-is-human-to-study-your-errors-is-glorious/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>X-raying the geometric precision error of DEMs with Fourier analysis</title>
		<link>http://www.corrada.com/blog/2008/02/02/x-raying-the-geometric-precision-error-of-dems-with-fourier-analysis/</link>
		<comments>http://www.corrada.com/blog/2008/02/02/x-raying-the-geometric-precision-error-of-dems-with-fourier-analysis/#comments</comments>
		<pubDate>Sat, 02 Feb 2008 18:10:32 +0000</pubDate>
		<dc:creator>Andrés</dc:creator>
				<category><![CDATA[Compressed Sensing]]></category>
		<category><![CDATA[Error Theory]]></category>
		<category><![CDATA[Fourier Analysis]]></category>
		<category><![CDATA[Group Theory]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Randomness]]></category>
		<category><![CDATA[DEMs]]></category>
		<category><![CDATA[symmetry group]]></category>
		<category><![CDATA[theory of geometric errors]]></category>

		<guid isPermaLink="false">http://www.corrada.com/blog/2008/02/02/x-raying-the-geometric-precision-error-of-dems-with-fourier-analysis/</guid>
		<description><![CDATA[In a previous post I mentioned a way of Fourier analyzing the geometric precision error of DEMs. Today I realized that the scheme I proposed can only account for part of the error signal. The approach I proposed is correct but it can only capture one particular aspect of the total error. The simplest way [...]]]></description>
		<wfw:commentRss>http://www.corrada.com/blog/2008/02/02/x-raying-the-geometric-precision-error-of-dems-with-fourier-analysis/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
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