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<rss version="2.0" xmlns:wfw="http://wellformedweb.org/CommentAPI/"><channel><title>mloss.org The Generalised Linear Models Inference and Estimation Toolbox</title><link>http://mloss.org</link><description>Updates and additions to The Generalised Linear Models Inference and Estimation Toolbox</description><language>en</language><lastBuildDate>Wed, 19 Oct 2011 22:26:05 -0000</lastBuildDate><item><title>The Generalised Linear Models Inference and Estimation Toolbox 1.4</title><link>http://mloss.org/software/view/269/</link><description>&lt;html&gt;&lt;p&gt;The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and SLMs (sparse linear models) as well as an implementation of a scalable convex variational Bayesian inference relaxation. We designed the glm-ie package to be simple, generic and easily expansible. Most of the code is written in Matlab including some MEX files. The code is fully compatible to both Matlab 7.x and GNU Octave 3.2.x.
&lt;/p&gt;
&lt;p&gt;Probabilistic classification, sparse linear modelling and logistic regression are covered in a common algorithmical framework allowing for both MAP estimation and approximate Bayesian inference.
&lt;/p&gt;&lt;/html&gt;</description><dc:creator xmlns:dc="http://purl.org/dc/elements/1.1/">hannes nickisch</dc:creator><pubDate>Wed, 19 Oct 2011 22:26:05 -0000</pubDate><comments>http://mloss.org/software/rss/comments/269</comments><guid>http://mloss.org/software/view/269/</guid><category>approximate inference</category><category>sparse learning</category><category>logistic regression</category></item></channel></rss>