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MBulli
2018-01-29 22:21:05 +01:00
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@@ -15,6 +15,10 @@
% correct bad hyphenation here
\hyphenation{op-tical net-works semi-conduc-tor}
\newcommand{\dop} [1]{\ensuremath{ \mathop{\mathrm{d}#1} }}
\newcommand{\R} {\ensuremath{ \mathbf{R} }}
\begin{document}
%
% paper title
@@ -29,22 +33,16 @@
% author names and affiliations
% use a multiple column layout for up to three different
% affiliations
\author{\IEEEauthorblockN{Michael Shell}
\IEEEauthorblockA{School of Electrical and\\Computer Engineering\\
Georgia Institute of Technology\\
Atlanta, Georgia 30332--0250\\
Email: http://www.michaelshell.org/contact.html}
\and
\IEEEauthorblockN{Homer Simpson}
\IEEEauthorblockA{Twentieth Century Fox\\
Springfield, USA\\
Email: homer@thesimpsons.com}
\and
\IEEEauthorblockN{James Kirk\\ and Montgomery Scott}
\IEEEauthorblockA{Starfleet Academy\\
San Francisco, California 96678--2391\\
Telephone: (800) 555--1212\\
Fax: (888) 555--1212}}
\author{
\IEEEauthorblockN{Markus Bullmann, Toni Fetzer, Frank Ebner, and Frank Deinzer}%
\IEEEauthorblockA{%
Faculty of Computer Science and Business Information Systems\\
University of Applied Sciences W\"urzburg-Schweinfurt\\
W\"urzburg, Germany\\
\{markus.bullmann, toni.fetzer, frank.ebner, frank.deinzer\}@fhws.de\\
}
}
\maketitle
@@ -70,29 +68,49 @@ The abstract goes here.
\section{Introduction}
% no \IEEEPARstart
This demo file is intended to serve as a ``starter file''
for IEEE conference papers produced under \LaTeX\ using
IEEEtran.cls version 1.8b and later.
% You must have at least 2 lines in the paragraph with the drop letter
% (should never be an issue)
I wish you the best of success.
\hfill mds
\hfill August 26, 2015
\subsection{Subsection Heading Here}
Subsection text here.
\subsubsection{Subsubsection Heading Here}
Subsubsection text here.
% KDE wellknown nonparametic estimation method
% Flexibility is paid with slow speed
% Finding optimal bandwidth
% Expensive computation
\section{Related work}
% original work rosenblatt/parzen
% binned version silverman, scott, härdle
% -> Fourier transfom
% other approaches Fast Gaussian Transform
\section{Kernel Density Estimation}
% KDE by rosenblatt and parzen
% general KDE
% Gauss Kernel
% Formula Gauss KDE
% -> complexity/operation count
% Binned KDE
% Binned Gauss KDE
% -> complexity/operation count
The histogram is a simple and for a long time the most used non-parametric estimator.
However, its inability to produce a continuous estimate dismisses it for many applications where a smooth distribution is assumed.
In contrast, the KDE is often the preferred tool because of its ability to produce a continuous estimate and its flexibility.
Given $n$ independently observed realizations of the observation set $X=(x_1,\dots,x_n)$, the kernel density estimate $\hat{f}_n$ of the density function $f$ of the underlying distribution is given with
\begin{equation}
\label{eq:kede}
\hat{f}_n = \frac{1}{nh} \sum_{i=1}^{n} K \left( \frac{x-X_i}{h} \right) \text{,} %= \frac{1}{n} \sum_{i=1}^{n} K_h(x-x_i)
\end{equation}
where $K$ is the kernel function and $h\in\R^+$ is an arbitrary smoothing parameter called bandwidth.
While any density function can be used as the kernel function $K$ (such that $\int K(u) \dop{u} = 1$), a variety of popular choices of the kernel function $K$ exits.
Commonly the Gaussian kernel is used.
\section{Box Filter}
% Basic box filter formula
% Recursive form
% Gauss Blur Filter
% Repetitive Box filter to approx Gauss
% Simple multipass, n/m approach, extended box filter
\section{Combination}
\section{Bla}
\section{Blub}
\section{Experiments}