0464463 20240903170538.320161029235959.9 85020286021 000388307600001 10.14311/NNW.2016.26.024 On-line mixture-based alternative to logistic regression 20 s. E cav_un_epca*02903211210-0552Neural Network World265 (2016)417-437Ústav informatiky AV ČR, v. v. i. on-line modeling on-line logistic regression recursive mixture estimation data dependent pointer cav_un_auth*0101167 Zpracování signálů Department of Signal Processing ZS ZS Department of Signal Processing Nagy Ivan UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0108105 Zpracování signálů Department of Signal Processing ZS ZS Department of Signal Processing Suzdaleva Evgenia UTIA-B RU Ústav teorie informace a automatizace AV ČR, v. v. i. pdf http://library.utia.cas.cz/separaty/2016/ZS/suzdaleva-0464463.pdf cav_un_auth*0321440 GA15-03564S GA ČR 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. BB 2017 2 RVO:67985556 http://hdl.handle.net/11104/0263657 S 3+4 Article Computer Science Artificial Intelligence COMPUTERSCIENCE.ARTIFICIALINTELLIGENCE 0.497 0.257 7.1 0.00023 0.091 251 0.174 Q4 0.394 35 Q4 2.277 3.4 Q4 Q4 3.383 2016 85020286021 SCOPUS 000388307600001 WOS pdf cav_un_epca*0290321 Neural Network World 1210-0552 Roč. 26 č. 5 2016 417 437 Ústav informatiky AV ČR, v. v. i.