2
0
Fork 0
mirror of https://github.com/MartinThoma/LaTeX-examples.git synced 2025-04-19 11:38:05 +02:00
LaTeX-examples/documents/papers/write-math-paper/ch7-mfrdb-eval.tex
2015-10-14 14:46:02 +02:00

32 lines
No EOL
1.3 KiB
TeX

%!TEX root = write-math-ba-paper.tex
\section{Evaluation}
The optimized classifier was evaluated on three publicly available data sets:
\verb+MfrDB_Symbols_v1.0+ \cite{Stria2012}, CROHME~2011 \cite{Mouchere2011},
and CROHME~2012 \cite{Mouchere2012}.
\verb+MfrDB_Symbols_v1.0+ contains recordings for 105~symbols, but for
11~symbols less than 50~recordings were available. For this reason, the
optimized classifier was evaluated on 94~of the 105~symbols.
The evaluation results are given in \cref{table:public-eval-results}.
\begin{table}[htb]
\centering
\begin{tabular}{lcrr}
\toprule
\multirow{2}{*}{Dataset} & \multirow{2}{*}{Symbols} & \multicolumn{2}{c}{Classification error}\\
\cmidrule(l){3-4}
& & Top-1 & Top-3 \\\midrule
MfrDB & 94 & $\SI{8.4}{\percent}$ & $\SI{1.3}{\percent}$ \\
CROHME 2011 & 56 & $\SI{10.2}{\percent}$ & $\SI{3.7}{\percent}$ \\
CROHME 2012 & 75 & $\SI{12.2}{\percent}$ & $\SI{4.1}{\percent}$ \\
\bottomrule
\end{tabular}
\caption{Error rates of the optimized recognizer systems. The systems
output layer was adjusted to the number of symbols it should
recognize and trained with the combined data from
write-math and the training given by the datasets.}
\label{table:public-eval-results}
\end{table}