Number of components and initialization in Gaussian mixture model for pattern recognition.
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F01%3A16010032" target="_blank" >RIV/67985556:_____/01:16010032 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21260/01:06071290
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Number of components and initialization in Gaussian mixture model for pattern recognition.
Original language description
The method for complete mixture initialization based on a product kernel estimate of probability density function is proposed for mixture estimation using EM-algorithm. The mixture components are assumed to correspond to local maxima of optimaly smoothedkernel density estimate. The gradient method is used for local extrema finding. As the last step, agglomerative hiearchical clustering methods merges closest components together. A comparison to scale-space approaches is given on examples.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2001
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Artificial Neural Nets and Genetic Algorithms. Proceedings.
ISBN
3-211-83651-9
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
406-409
Publisher name
Springer
Place of publication
Wien
Event location
Prague [CZ]
Event date
Apr 22, 2001
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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