diff options
| -rw-r--r-- | experimental.tex | 2 | ||||
| -rw-r--r-- | uniqueness.tex | 4 |
2 files changed, 3 insertions, 3 deletions
diff --git a/experimental.tex b/experimental.tex index d46270a..f513b92 100644 --- a/experimental.tex +++ b/experimental.tex @@ -174,7 +174,7 @@ permit a proper training of the algorithm. \includegraphics[]{graphics/offline-sht.pdf} \label{fig:offline:sht} } - \caption{Results with 10-fold cross validation for the top $n_p$ most present people} + \caption{Results with 10-fold cross-validation for the top $n_p$ most present people} \label{fig:offline} \end{center} \vspace{-1.5\baselineskip} diff --git a/uniqueness.tex b/uniqueness.tex index d3b4822..66f21a4 100644 --- a/uniqueness.tex +++ b/uniqueness.tex @@ -60,12 +60,12 @@ threshold, and \emph{unmatched} otherwise. Formally, let $(\bs_1,\bs_2)$ be an input pair of the algorithm ($\bs_i\in\mathbf{R}_+^{6}$, are the six bone measurements), the output of the algorithm for the threshold $\delta$ is defined as: -\begin{displaymath} +\begin{equation} A_\delta(\bs_1,\bs_2) = \begin{cases} 1 & \text{if $d(\bs_1,\bs_2) < \delta$}\\ 0 & \text{otherwise} \end{cases} -\end{displaymath} +\end{equation} \begin{figure}[t] \begin{center} |