Classification by the Use of Decomposition of Correlation Integral

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F09%3A00173045" target="_blank" >RIV/68407700:21460/09:00173045 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985807:_____/09:00342904

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Classification by the Use of Decomposition of Correlation Integral

Popis výsledku v původním jazyce

The correlation dimension is usually used to study features of fractals and data generating processes. For estimating the value of the correlation dimension in a particular case, a polynomial approximation of correlation integral is often used and then linear regression for logarithms of variables is applied. In this Chapter, we show that the correlation integral can be decomposed into functions each related to a particular point of data space. For these functions, one can use similar polynomial approximations such as the correlation integral. The essential difference is that the value of the exponent, which would correspond to the correlation dimension, differs in accordance to the position of the point in question. Moreover, we show that the multiplicative constant represents the probability density estimation at that point. This finding is used to construct a classifier. Tests with some data sets from the Machine Learning Repository show that this classifier can be very effective.

Název v anglickém jazyce

Classification by the Use of Decomposition of Correlation Integral

Popis výsledku anglicky

The correlation dimension is usually used to study features of fractals and data generating processes. For estimating the value of the correlation dimension in a particular case, a polynomial approximation of correlation integral is often used and then linear regression for logarithms of variables is applied. In this Chapter, we show that the correlation integral can be decomposed into functions each related to a particular point of data space. For these functions, one can use similar polynomial approximations such as the correlation integral. The essential difference is that the value of the exponent, which would correspond to the correlation dimension, differs in accordance to the position of the point in question. Moreover, we show that the multiplicative constant represents the probability density estimation at that point. This finding is used to construct a classifier. Tests with some data sets from the Machine Learning Repository show that this classifier can be very effective.

Druh

C - Kapitola v odborné knize

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/1M0567" target="_blank" >1M0567: Centrum aplikované kybernetiky</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2009

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Foundations of Computational Intelligence: Studies in Computational Intelligence

ISBN

978-3-642-01535-9

Počet stran výsledku

17

Strana od-do

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Počet stran knihy

380

Název nakladatele

Springer

Místo vydání

Berlin

Kód UT WoS kapitoly

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