Add initial abstract
This commit is contained in:
parent
bb1b40ab2e
commit
1ef305861d
@ -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}
|
||||
|
Reference in New Issue
Block a user