\documentclass[a4paper]{article}
\usepackage{ri-paper}

\title{Relevance Preprocessing for Generalized Learning Vector Quantization Methods.}

\author{J.F. van Wezel,\\S1914146}

\affil{University of Groningen}
\date{\today}

\begin{document}

\maketitle

\begin{abstract}
\noindent Datasets often contain statistical regularities. These regularities can be used to improve the distance metric in Learning Vector Quantization (LVQ) with a relevance matrix. Methods like Global Metric Learning (GML) and Supervised Variational Relevance Learning (SUVREL) try to find this matrix. We tried to find if various LVQ methods would converge faster with a predetermined relevance matrix on multiple datasets. We found that the various LVQ methods did not converge noticeably faster with a predetermined matrix but achieved a better classification result on all but one of the tested datasets. Furthermore a variant of SUVREL, called SUVREL Localized, is introduced and tested. It showed comparable performance to SUVREL and GML.
\end{abstract}

\smallskip
\noindent \textbf{Preprocessing - Generalized Learning Vector Quantization - Metric Learning - Relevance Matrix - Gradient Descent - Machine Learning}

\input{sections/introduction}
\section{Method} % (fold)
\label{sec:method}

This section will shortly recap the used methods. It will first explain the used prepossessing methods GML and SUVREL as well as an altered version of SUVREL that is capable of finding the class-wise relevances of a dataset. Then we will go over the used classifiers: K-nn and the various LVQ methods.
\input{sections/methods/gml}
\input{sections/methods/suvrel}
\input{sections/methods/suvrell}

\input{sections/methods/knn}

\input{sections/methods/lvq}
	%subsection under lvq:
	\input{sections/methods/glvq}
	\input{sections/methods/rglvq}
	\input{sections/methods/gmlvq}
	\input{sections/methods/lgmlvq}
	\input{sections/methods/fnorm}
%\input{sections/datasets}
\input{sections/experiments}
\input{sections/results}
\input{sections/discussion}
\input{sections/conclusion}
\input{sections/future-work}

% section conclusion (end)
\bibliographystyle{plain}
\bibliography{bibliography}

% \begin{figure}[hbt]
% 	\centering
% 		\includegraphics[width=0.5\textwidth]{./img/colormap/colormap.png}
% 		\caption{Colourmap which maps (initial) values of $P$ and $x$ to unique colours.}
% 		\label{fig:app:colormap}
% \end{figure}

\end{document}
