Original title: Comparison of mixture-based classification with the data-dependent pointer model for various types of components
Authors: Likhonina, Raissa ; Suzdaleva, Evgenia ; Nagy, Ivan
Document type: Research reports
Year: 2016
Language: eng
Series: Research Report, volume: 2355
Abstract: The presented report is devoted to the analysis of a data-dependent pointer model, whether it brings some advantages in comparison with a data-independent pointer model at simulation and estimation of components referring to different types of distribution, including categorical, uniform, exponential and state-space components for a dynamic data-dependent model, and normal components for a static data-dependent pointer model.
Keywords: data-dependent pointer; mixture-based classification; recurisive mixture estimation
Project no.: GA15-03564S (CEP)
Funding provider: GA ČR

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2016/ZS/suzdaleva-0462164.pdf
Original record: http://hdl.handle.net/11104/0262264

Permalink: http://www.nusl.cz/ntk/nusl-260852


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Research > Institutes ASCR > Institute of Information Theory and Automation
Reports > Research reports