Add intro and references to the background section

mini_proj/report/references.bib

@@ -0,0 +1,35 @@
+@techreport{knn,
+  title={Discriminatory analysis-nonparametric discrimination: consistency properties},
+  author={Fix, Evelyn and Hodges Jr, Joseph L},
+  year={1951},
+  institution={California Univ Berkeley}
+}
+@article{svm,
+  title={Support-vector networks},
+  author={Cortes, Corinna and Vapnik, Vladimir},
+  journal={Machine learning},
+  volume={20},
+  number={3},
+  pages={273--297},
+  year={1995},
+  publisher={Springer}
+}
+@article{naivebayes,
+  title={Idiot's Bayes—not so stupid after all?},
+  author={Hand, David J and Yu, Keming},
+  journal={International statistical review},
+  volume={69},
+  number={3},
+  pages={385--398},
+  year={2001},
+  publisher={Wiley Online Library}
+}
+@article{randomforest,
+  title={Classification and regression by randomForest},
+  author={Liaw, Andy and Wiener, Matthew and others},
+  journal={R news},
+  volume={2},
+  number={3},
+  pages={18--22},
+  year={2002}
+}

mini_proj/report/waldo.tex

@@ -19,6 +19,9 @@
 	\usepackage{bookmark}
 	\usepackage{natbib}
 
+	\usepackage{xcolor}
+	\newcommand{\todo}[1]{\marginpar{{\textsf{TODO}}}{\textbf{\color{red}[#1]}}}
+
 	\begin{document}
 		\title{What is Waldo?}
 		\author{Kelvin Davis \and Jip J. Dekker\and Anthony Silvestere}
@@ -46,8 +49,10 @@
 		\begin{figure}[ht]
 			\includegraphics[scale=0.35]{waldo}
 			\centering
-			\caption{A headshot of the character ``Waldo'', or ``Wally''. Pictures of
+			\caption{
-			Waldo copyrighted by Martin Handford used under the fair-use policy.}
+				A headshot of the character ``Waldo'', or ``Wally''. Pictures of Waldo
+				copyrighted by Martin Handford and are used under the fair-use policy.
+			}
 			\label{fig:waldo}
 		\end{figure}
 
@@ -74,13 +79,49 @@
 
 		\section{Background} \label{sec:background}
 
+		The classification methods used can separated into two separate groups:
+		classical machine learning methods and neural network architectures. Many of
+		the classical machine learning algorithms have variations and improvements
+		for various purposes; however, for this report we will be using their only
+		their basic versions. In contrast, we will use different neural network
+		architectures, as this method is currently the most used for image
+		classification.
+
+		\subsection{Classical Machine Learning Methods}
+
+		\paragraph{Naive Bayes Classifier}
+
+		\cite{naivebayes}
+
+		\paragraph{$k$-Nearest Neighbors}
+
+		($k$-NN) \cite{knn}
+
+		\paragraph{Support Vector Machine}
+
+		\cite{svm}
+
+		\paragraph{Random Forest}
+
+		\cite{randomforest}
+
+		\subsection{Neural Network Architectures}
+		\todo{Did we only do the three in the end? (Alexnet?)}
+
+		\paragraph{Convolutional Neural Networks}
+
+		\paragraph{LeNet}
+
+		\paragraph{Fully Convolutional Neural Networks}
+
+
 		\section{Methods} \label{sec:methods}
 
 		\section{Results and Discussion} \label{sec:results}
 
 		\section{Conclusion} \label{sec:conclusion}
 
-		\bibliographystyle{humannat}
+		\bibliographystyle{alpha}
 		\bibliography{references}
 
 	\end{document}