Classification by the Use of Decomposition of Correlation Integral
The result's identifiers
Result code in 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>
Alternative codes found
RIV/67985807:_____/09:00342904
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Classification by the Use of Decomposition of Correlation Integral
Original language description
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.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/1M0567" target="_blank" >1M0567: Centre for Applied Cybernetics</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2009
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Foundations of Computational Intelligence: Studies in Computational Intelligence
ISBN
978-3-642-01535-9
Number of pages of the result
17
Pages from-to
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Number of pages of the book
380
Publisher name
Springer
Place of publication
Berlin
UT code for WoS chapter
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