On-line mixture-based alternative to logistic regression

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F16%3A00464463" target="_blank" >RIV/67985556:_____/16:00464463 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.14311/NNW.2016.26.024" target="_blank" >http://dx.doi.org/10.14311/NNW.2016.26.024</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/NNW.2016.26.024" target="_blank" >10.14311/NNW.2016.26.024</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On-line mixture-based alternative to logistic regression

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

On-line mixture-based alternative to logistic regression

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

<a href="/cs/project/GA15-03564S" target="_blank" >GA15-03564S: Shlukování a klasifikace s využitím rekurzivního odhadování modelu směsi distribucí</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Neural Network World

ISSN

1210-0552

e-ISSN

—

Svazek periodika

26

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

20

Strana od-do

417-437

Kód UT WoS článku

000388307600001

EID výsledku v databázi Scopus

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