Efficient implementation of compositional models for data mining

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F18%3A00497540" target="_blank" >RIV/67985556:_____/18:00497540 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Efficient implementation of compositional models for data mining

Popis výsledku v původním jazyce

A compositional model encodes probabilistic relationships among variables of interest. In connection with various statistical techniques, it represents a practical tool for data modeling and data mining. Structure of the model represents (un)conditional independencies among all variables. Relationships of dependent variables are described by low-dimensional probability distributions. Having a compositional model, a data miner can easily apply an intervention on variables of interest, fix values of other variables (conditioning), or to narrow the context of a problem (marginalization). The model learning process can be controlled to avoid overfitting of data.nnIn this paper, we present a new semi-supervised web application that will enable researchers to design probabilistic (compositional) models (both causal and stochastic). Thanks to the web architecture of the system, the researchers will always have a possibility to influence the data-based model construction process from any place of the world. It is also expected that the application of this methodology to practical problems will open new problems that will be an inspiration for further theoretical research.

Název v anglickém jazyce

Efficient implementation of compositional models for data mining

Popis výsledku anglicky

A compositional model encodes probabilistic relationships among variables of interest. In connection with various statistical techniques, it represents a practical tool for data modeling and data mining. Structure of the model represents (un)conditional independencies among all variables. Relationships of dependent variables are described by low-dimensional probability distributions. Having a compositional model, a data miner can easily apply an intervention on variables of interest, fix values of other variables (conditioning), or to narrow the context of a problem (marginalization). The model learning process can be controlled to avoid overfitting of data.nnIn this paper, we present a new semi-supervised web application that will enable researchers to design probabilistic (compositional) models (both causal and stochastic). Thanks to the web architecture of the system, the researchers will always have a possibility to influence the data-based model construction process from any place of the world. It is also expected that the application of this methodology to practical problems will open new problems that will be an inspiration for further theoretical research.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/GA16-12010S" target="_blank" >GA16-12010S: Struktury podmíněné nezávislosti: kombinatorické a optimalizační metody</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 21st Czech-Japan Seminar od Data Analysis and Decision Making

ISBN

978-80-7464-932-5

ISSN

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e-ISSN

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Počet stran výsledku

8

Strana od-do

80-87

Název nakladatele

Aoyama Gakuin University, Japan

Místo vydání

Japan

Místo konání akce

Kamakura

Datum konání akce

23. 11. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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