Correlation Dimension Estimation for Classification

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Correlation Dimension Estimation for Classification

Popis výsledku v původním jazyce

In this paper we introduce a new approach that utilizes the correlation dimension (CD) both for probability density estimate of data and consequently for classification. It will be shown that just a classifier utilizing CD exhibits significantly better behavior (classification accuracy) then other kinds of classifiers. Correlation dimension is used for complexity estimation of fractals or any other data generating processes. Basic idea was introduced in the well known paper by Grassberger and Procacciaand there are papers dealing with the correlation dimension estimation. Some experiments that use the correlation dimension for classification has been published too. The idea of correlation dimension classifier directly follows the principle of classifier, which uses the distribution mapping exponent (DME). It is shown that CD-based classifier outperforms DME classifier and many classifiers published on Machine Learning Repository pages.

Název v anglickém jazyce

Correlation Dimension Estimation for Classification

Popis výsledku anglicky

In this paper we introduce a new approach that utilizes the correlation dimension (CD) both for probability density estimate of data and consequently for classification. It will be shown that just a classifier utilizing CD exhibits significantly better behavior (classification accuracy) then other kinds of classifiers. Correlation dimension is used for complexity estimation of fractals or any other data generating processes. Basic idea was introduced in the well known paper by Grassberger and Procacciaand there are papers dealing with the correlation dimension estimation. Some experiments that use the correlation dimension for classification has been published too. The idea of correlation dimension classifier directly follows the principle of classifier, which uses the distribution mapping exponent (DME). It is shown that CD-based classifier outperforms DME classifier and many classifiers published on Machine Learning Repository pages.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2006

Kód důvěrnosti údajů

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

Název statě ve sborníku

The 8th International Conference on Artificial Intelligence and Soft Computing

ISBN

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ISSN

1895-8648

e-ISSN

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Počet stran výsledku

11

Strana od-do

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Název nakladatele

Held

Místo vydání

Zakopané

Místo konání akce

Zakopane

Datum konání akce

25. 6. 2006

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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