From 1ef305861d931a98b410371c6d61b959d8e365b7 Mon Sep 17 00:00:00 2001
From: "Jip J. Dekker" <jip@dekker.li>
Date: Fri, 25 May 2018 12:13:57 +1000
Subject: [PATCH] Add initial abstract

---
 mini_proj/report/waldo.tex | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/mini_proj/report/waldo.tex b/mini_proj/report/waldo.tex
index 2b101de..b4b1e81 100644
--- a/mini_proj/report/waldo.tex
+++ b/mini_proj/report/waldo.tex
@@ -24,11 +24,23 @@
 
 	\begin{document}
 		\title{What is Waldo?}
-		\author{Kelvin Davis \and Jip J. Dekker\and Anthony Silvestere}
+		\author{Kelvin Davis \and Jip J. Dekker \and Anthony Silvestere}
 		\maketitle
 
 		\begin{abstract}
-
+%
+		The famous brand of picture puzzles ``Where's Waldo?'' relates well to many
+		unsolved image classification problem. This offers us the opportunity to
+		test different image classification methods on a data set that is both small
+		enough to compute in a reasonable time span and easy for humans to
+		understand. In this report we compare the well known machine learning
+		methods Naive Bayes, Support Vector Machines, $k$-Nearest Neighbors, and
+		Random Forest against the Neural Network Architectures LeNet, Fully
+		Convolutional Neural Networks, and Fully Convolutional Neural Networks.
+		\todo{I don't like this big summation but I think it is the important
+		information}
+		Our comparison shows that \todo{...}
+%
 		\end{abstract}
 
 		\section{Introduction}