From 963c5190b75f5bc2e7557a2da13a400cf1f17461 Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Sat, 30 Mar 2013 18:53:00 +0100
Subject: Typo fixes

---
 experimental.tex | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'experimental.tex')

diff --git a/experimental.tex b/experimental.tex
index a7871eb..dd5a73c 100644
--- a/experimental.tex
+++ b/experimental.tex
@@ -176,7 +176,7 @@ varying group size $n_p = \{3,5,10,25\}$.
 
 
 \fref{fig:offline} shows the precision-recall plot as the threshold varies.
-Both algrithms perform three times better than the majority class baseline of
+Both algorithms perform three times better than the majority class baseline of
 15\% with a recall of 100\% on all people.  We make two main observations.
 First, as expected, SHT performs better than MoG because of temporal smoothing.
 Second, performance is inversely proportional to group size.  As we test
@@ -364,7 +364,7 @@ given the relatively low resolution of the Kinect's infrared camera.
 
 \fref{fig:var} compares the precision-recall curve of \fref{fig:offline:sht} to
 the curve of the same experiment run on the newly obtained dataset. We observe
-a roughly 20\% increase in performace across most thresholds.  We
+a roughly 20\% increase in performance across most thresholds.  We
 believe these results would significantly outperform face recognition
 in a similar setting.
 
-- 
cgit v1.2.3-70-g09d2

