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<title>Inductive Logic Programming: from Machine Learning to Software Engineering</title>
<link>http://sange.fi/~atehwa/cgi-bin/piki.cgi/</link>
<description>Recent changes in Inductive Logic Programming: from Machine Learning to Software Engineering</description>
<item><title>Inductive Logic Programming: from Machine Learning to Software Engineering</title>
<link>http://sange.fi/~atehwa/cgi-bin/piki.cgi/Inductive%20Logic%20Programming%3A%20from%20Machine%20Learning%20to%20Software%20Engineering</link>
<guid>http://sange.fi/~atehwa/cgi-bin/piki.cgi/#1183657230</guid>
<description>&lt;p&gt;[...]

&lt;p&gt;&lt;del&gt;A&lt;/del&gt; &lt;ins&gt;(((A&lt;/ins&gt; program, its examples, and the 
procedures that are used to produce one from the other are naturally 
intimately connected. A procedure that produces a program from examples 
is an inductive inference machine IIM(''e'') = ''P''; a procedure that 
produces a set of examples from a program is a test set generation 
procedure TSG(''P'') = ''e''. ILP usually studies the construction of a 
known program. Basically, it could study the selection of an optimal 
example set for a given inductive inference machine, but usually 
studies the selection of the optimal IIM for a given set of examples 
(which is usually quite arbitrarily chosen). For induction, we could 
also study the optimal program to be produced for a given set of 
examples -- i.e. what the "correct" result of an IIM actually 
&lt;del&gt;is.&lt;/del&gt; &lt;ins&gt;is.)))&lt;/ins&gt; 

&lt;p&gt;[...]

</description>
<pubDate>Thu, 05 Jul 2007 17:40:30 +0000</pubDate>
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