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