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On-line mixture-based alternative to logistic regression

The result's identifiers

  • Result code in 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>

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

    On-line mixture-based alternative to logistic regression

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    BB - Applied statistics, operational research

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/GA15-03564S" target="_blank" >GA15-03564S: Clustering and classification using recursive mixture estimation</a><br>

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2016

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Neural Network World

  • ISSN

    1210-0552

  • e-ISSN

  • Volume of the periodical

    26

  • Issue of the periodical within the volume

    5

  • Country of publishing house

    CZ - CZECH REPUBLIC

  • Number of pages

    20

  • Pages from-to

    417-437

  • UT code for WoS article

    000388307600001

  • EID of the result in the Scopus database