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| author | Timotej Lazar <timotej.lazar@fri.uni-lj.si> | 2018-02-04 12:30:43 +0100 |
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| committer | Timotej Lazar <timotej.lazar@fri.uni-lj.si> | 2018-02-04 12:30:43 +0100 |
| commit | 420d2e988ef93e0117a006287cf68c6e107286f7 (patch) | |
| tree | a2a5b2ba4e79a3af6bf6e22c19a3f64e04542d97 /aied2018/rules.tex | |
| parent | b07decf5055486a55d75c6ff833d46ba2f13f88b (diff) | |
Add introduction
Diffstat (limited to 'aied2018/rules.tex')
| -rw-r--r-- | aied2018/rules.tex | 1 |
1 files changed, 1 insertions, 0 deletions
diff --git a/aied2018/rules.tex b/aied2018/rules.tex index 7821ec4..572d261 100644 --- a/aied2018/rules.tex +++ b/aied2018/rules.tex @@ -1,4 +1,5 @@ \section{Rules} +\label{sec:rules} \subsection{The learning algorithm} The goal of learning rules in this paper is to extract and explain common approaches and mistakes in student programs. We use a rule learner called ABCN2e implemented within the Orange data mining library~\cite{demsar2013orange}. ABCN2e is an improvement of the classical CN2 algorithm~\cite{clarkECML1991} for learning unordered rules. The differences between CN2 and ABCN2e are described in a technical report found at \url{https://ailab.si/abml.} |