From 1c8228f891a04fcb9d66ac5ffc4a6291f662c330 Mon Sep 17 00:00:00 2001 From: Timotej Lazar Date: Sun, 16 Apr 2017 15:47:27 +0200 Subject: Fix paper URL --- paper/evaluation.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/evaluation.tex b/paper/evaluation.tex index 3b7f9fb..077b684 100644 --- a/paper/evaluation.tex +++ b/paper/evaluation.tex @@ -35,7 +35,7 @@ The bottom two rows give aggregated results (total and average) over all 44 doma \label{table:eval} \end{table} -Table~\ref{table:eval} contains results on five selected problems (each representing a group of problems from one lab session), and averaged results over all 44 problems.\footnote{We report only a subset of results due to space restrictions. Full results and source code can be found at \url{https://ailab.si/ast-patterns/}. } The second, third, and fourth columns provide classification accuracies (CA) of the rule-based, majority, and random-forest classifiers on testing data. The majority classifier and the random forests method, +Table~\ref{table:eval} contains results on five selected problems (each representing a group of problems from one lab session), and averaged results over all 44 problems.\footnote{We report only a subset of results due to space restrictions. Full results and source code can be found at \url{https://ailab.si/aied2017}. } The second, third, and fourth columns provide classification accuracies (CA) of the rule-based, majority, and random-forest classifiers on testing data. The majority classifier and the random forests method, which had the best overall performance, serve as references for bad and good CA on particular data sets. For example, our rules correctly classified 99\% of testing instances for the \code{sister} problem, -- cgit v1.2.1