Number of the records: 1
Comparison of mixture-based classification with the data-dependent pointer model for various types of components
- 1.0462164 - ÚTIA 2017 CZ eng V - Research Report
Likhonina, Raissa - Suzdaleva, Evgenia - Nagy, Ivan
Comparison of mixture-based classification with the data-dependent pointer model for various types of components.
Praha: ÚTIA AV ČR, 2016. 59 s. Research Report, 2355.
R&D Projects: GA ČR GA15-03564S
Institutional support: RVO:67985556
Keywords : mixture-based classification * data-dependent pointer * recurisive mixture estimation
Subject RIV: BB - Applied Statistics, Operational Research
http://library.utia.cas.cz/separaty/2016/ZS/suzdaleva-0462164.pdf
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.
Permanent Link: http://hdl.handle.net/11104/0262264
File Download Size Commentary Version Access 0462164.pdf 1 7.9 MB Other open-access
Number of the records: 1