From fff7a0b8c20a3de752cc038e6ae2b784a2f46712 Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Wed, 29 Feb 2012 12:12:48 -0800
Subject: Minor improvements in the uniqueness section

---
 uniqueness.tex | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

(limited to 'uniqueness.tex')

diff --git a/uniqueness.tex b/uniqueness.tex
index dae88c3..c96cf51 100644
--- a/uniqueness.tex
+++ b/uniqueness.tex
@@ -93,7 +93,7 @@ pairs with a false positive rate of 6\%.
 This experiment gives an idea of the noise variance level above which
 it is not possible to consistently distinguish skeletons. This noise
 level can be interpreted as follows in the person recognition
-setting. For this problem, a classifier can be built by first learning
+problem. For this problem, a classifier can be built be first learning
 a \emph{skeleton profile} for each individual from all the
 measurements in the training set. Then, given a new skeleton
 measurement, the algorithm classifies it to the individual whose
@@ -105,9 +105,9 @@ there are two distinct sources of noise:
 \item the noise of the new measurement: this comes from the device
   doing the measurement.
 \end{itemize}
-
-We will come back in section \label{sec:kinect} on the structure of
-the noise and its relation to the noise represented on the ROC curves.
+the combination of these two noises is what can be compared to the
+noise represented on the ROC curves. Section \label{sec:kinect} will
+give more insight on the structure of the noise.
 
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