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> <channel><title>puremango.co.uk &#187; machine learning</title> <atom:link href="http://www.puremango.co.uk/category/machine-learning/feed/" rel="self" type="application/rss+xml" /><link>http://www.puremango.co.uk</link> <description>innovative coding, tutorials, web stuff. celebrating 6 years online.</description> <lastBuildDate>Thu, 19 Jan 2012 18:35:13 +0000</lastBuildDate> <language>en</language> <sy:updatePeriod>hourly</sy:updatePeriod> <sy:updateFrequency>1</sy:updateFrequency> <generator>http://wordpress.org/?v=3.3.1</generator> <item><title>Genetic Algorithm Examples</title><link>http://www.puremango.co.uk/2011/03/genetic-algorithm-examples/</link> <comments>http://www.puremango.co.uk/2011/03/genetic-algorithm-examples/#comments</comments> <pubDate>Thu, 03 Mar 2011 23:28:03 +0000</pubDate> <dc:creator>Howard Yeend</dc:creator> <category><![CDATA[machine learning]]></category> <category><![CDATA[artificial intelligence]]></category> <category><![CDATA[computer science]]></category> <category><![CDATA[demo]]></category> <category><![CDATA[genetic algorithms]]></category> <category><![CDATA[machine]]></category> <category><![CDATA[optimisation]]></category> <category><![CDATA[tutorial]]></category> <guid
isPermaLink="false">http://www.puremango.co.uk/?p=1680</guid> <description><![CDATA[There&#8217;s been a lot of buzz recently on reddit and HN about genetic algorithms. Some impressive new demos have surfaced and I&#8217;d like to take this opportunity to review some of the cool things people have done with genetic algorithms, a fascinating subfield of evolutionary computing / machine learning (which is itself a part of [...]]]></description> <wfw:commentRss>http://www.puremango.co.uk/2011/03/genetic-algorithm-examples/feed/</wfw:commentRss> <slash:comments>10</slash:comments> </item> <item><title>Genetic Algorithm For Hello World</title><link>http://www.puremango.co.uk/2010/12/genetic-algorithm-for-hello-world/</link> <comments>http://www.puremango.co.uk/2010/12/genetic-algorithm-for-hello-world/#comments</comments> <pubDate>Tue, 14 Dec 2010 00:36:45 +0000</pubDate> <dc:creator>Howard Yeend</dc:creator> <category><![CDATA[javascript]]></category> <category><![CDATA[machine learning]]></category> <category><![CDATA[chromosome]]></category> <category><![CDATA[computer science]]></category> <category><![CDATA[fitness]]></category> <category><![CDATA[genetic algorithms]]></category> <category><![CDATA[hello world]]></category> <category><![CDATA[mutation]]></category> <category><![CDATA[optimisation]]></category> <guid
isPermaLink="false">http://www.puremango.co.uk/?p=1580</guid> <description><![CDATA[This article works through the creation of a &#8216;toy&#8217; genetic algorithm which starts with a few hundred random strings and evolves towards the phrase &#8220;Hello World!&#8221;. It&#8217;s a toy example because we know in advance what the optimum solution is &#8211; the phrase &#8220;Hello World!&#8221; &#8211; but it provides a nice simple introduction to evolutionary [...]]]></description> <wfw:commentRss>http://www.puremango.co.uk/2010/12/genetic-algorithm-for-hello-world/feed/</wfw:commentRss> <slash:comments>19</slash:comments> </item> <item><title>A quick overview of machine learning techniques</title><link>http://www.puremango.co.uk/2010/11/a-quick-overview-of-machine-learning-techniques/</link> <comments>http://www.puremango.co.uk/2010/11/a-quick-overview-of-machine-learning-techniques/#comments</comments> <pubDate>Sat, 27 Nov 2010 16:43:31 +0000</pubDate> <dc:creator>Howard Yeend</dc:creator> <category><![CDATA[machine learning]]></category> <category><![CDATA[artificial intelligence]]></category> <category><![CDATA[clustering]]></category> <category><![CDATA[genetic algorithms]]></category> <category><![CDATA[Joshua]]></category> <category><![CDATA[neural networks]]></category> <category><![CDATA[optimisation]]></category> <category><![CDATA[prediction]]></category> <category><![CDATA[WOPR]]></category> <guid
isPermaLink="false">http://www.puremango.co.uk/?p=1550</guid> <description><![CDATA[Machine learning is a fascinating discipline. Often inspired by natural processes, it can produce astounding results in a wide range of applications. Modern web search is underpinned by ML techniques such as clustering and statistical text processing. Computer games make use of evolutionary algorithms to produce better artificial enemies. Your camera probably has face detection [...]]]></description> <wfw:commentRss>http://www.puremango.co.uk/2010/11/a-quick-overview-of-machine-learning-techniques/feed/</wfw:commentRss> <slash:comments>0</slash:comments> </item> <item><title>XKCD Colour Survey &#8211; a 3D visualisation</title><link>http://www.puremango.co.uk/2010/05/xkcd-color-survey-3d-visualization/</link> <comments>http://www.puremango.co.uk/2010/05/xkcd-color-survey-3d-visualization/#comments</comments> <pubDate>Tue, 04 May 2010 20:48:47 +0000</pubDate> <dc:creator>Howard Yeend</dc:creator> <category><![CDATA[javascript]]></category> <category><![CDATA[machine learning]]></category> <category><![CDATA[canvas]]></category> <category><![CDATA[code]]></category> <category><![CDATA[images]]></category> <category><![CDATA[web]]></category> <guid
isPermaLink="false">http://www.puremango.co.uk/?p=938</guid> <description><![CDATA[Randall Munroe (of XKCD) has been running a &#8220;name the colour&#8221; survey for the last few months and today released the data. The results are broken down by gender, and he makes some fascinating obvservations. I&#8217;ve been working on a colour-related project on and off for about a year now, and part of that has [...]]]></description> <wfw:commentRss>http://www.puremango.co.uk/2010/05/xkcd-color-survey-3d-visualization/feed/</wfw:commentRss> <slash:comments>14</slash:comments> </item> <item><title>The K-Means Clustering Machine Learning Algorithm</title><link>http://www.puremango.co.uk/2010/01/k-means-clustering-machine-learning/</link> <comments>http://www.puremango.co.uk/2010/01/k-means-clustering-machine-learning/#comments</comments> <pubDate>Sun, 24 Jan 2010 20:28:13 +0000</pubDate> <dc:creator>Howard Yeend</dc:creator> <category><![CDATA[machine learning]]></category> <category><![CDATA[clustering]]></category> <category><![CDATA[k-means]]></category> <category><![CDATA[tutorial]]></category> <category><![CDATA[unsupervised learning]]></category> <guid
isPermaLink="false">http://www.puremango.co.uk/?p=714</guid> <description><![CDATA[The k-means clustering algorithm is one of the simplest unsupervised machine learning algorithms, which can be used to automatically recognise groups of similar points in data without any human intervention or training. The first step is to represent the data you want to group as points in an n-dimensional space, where n is the number [...]]]></description> <wfw:commentRss>http://www.puremango.co.uk/2010/01/k-means-clustering-machine-learning/feed/</wfw:commentRss> <slash:comments>9</slash:comments> </item> </channel> </rss>
