On-line mixture-based alternative to logistic regression

0464463 - ÚTIA 2017 RIV CZ eng J - Journal Article
Nagy, Ivan - Suzdaleva, Evgenia
On-line mixture-based alternative to logistic regression.
Neural Network World. Roč. 26, č. 5 (2016), s. 417-437. ISSN 1210-0552
R&D Projects: GA ČR GA15-03564S
Institutional support: RVO:67985556
Keywords : on-line modeling * on-line logistic regression * recursive mixture estimation * data dependent pointer
Subject RIV: BB - Applied Statistics, Operational Research
Impact factor: 0.394, year: 2016
http://library.utia.cas.cz/separaty/2016/ZS/suzdaleva-0464463.pdf

The paper deals with a problem of modeling discrete variables depending on continuous variables. This problem is known as the logistic regression estimated by numerical methods. The paper approaches the problem via the recursive Bayesian estimation of mixture models with the purpose of exploring a possibility of constructing the continuous data dependent switching model that should be estimated on-line. Here the model of the discrete variable dependent on continuous data is represented as the model of the mixture pointer dependent on data from mixture components via their parameters, which switch according to the activity of the components. On-line estimation of the data dependent pointer model has a great potential for tasks of clustering and classification. The specific subproblems include (i) the model parameter estimation both of the pointer and of the components obtained during the learning phase, and (ii) prediction of the pointer value during the testing phase.
Permanent Link: http://hdl.handle.net/11104/0263657