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Wyświetlanie 1-3 z 3
Tytuł:
The paradigm of statistical inference and the paradigm of statistical learning
Autorzy:
Pociecha, Józef
Powiązania:
https://bibliotekanauki.pl/articles/1806781.pdf
Data publikacji:
2021-08-24
Wydawca:
Główny Urząd Statystyczny
Tematy:
paradigm theory
learning from data
statistical inference
statistical learning
Opis:
The starting point for the presentation of the similarities and differences between the principles of conducting statistical research according to the rules of both statistical inference and statistical learning is the paradigm theory, formulated by Thomas Kuhn. In the first section of this paper, the essential features of the statistical inference paradigm are characterised, with particular attention devoted to its limitations in contemporary statistical research. Subsequently, the article presents the challenges faced by this research jointly with the expanding opportunities for their effective reduction. The essence of learning from data is discussed and the principles of statistical learning are defined. Moreover, significant features of the statistical learning paradigm are formulated in the context of the differences between the statistical inference paradigm and the statistical learning paradigm. It is emphasised that the statistical learning paradigm, as the more universal one of the two discussed, broadens the possibilities of conducting statistical research, especially in socio-economic sciences.
Źródło:
Przegląd Statystyczny; 2021, 68, 1; 1-16
0033-2372
Pojawia się w:
Przegląd Statystyczny
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Bayesian Network Modeling in Discovering Risk Factors of Dental Caries in Three-Year-Old Children
Autorzy:
Łaguna, W.
Bagińska, J.
Oniśko, A.
Powiązania:
https://bibliotekanauki.pl/articles/1918880.pdf
Data publikacji:
2019-08-26
Wydawca:
Uniwersytet Medyczny w Białymstoku
Tematy:
dental caries
Bayesian network
learning from data
risk assessment
Opis:
Purpose - The aim of this study was to use probabilistic graphical models to determine dental caries risk factors in three-year-old children. The analysis was conducted on the basis of the questionnaire data and resulted in building probabilistic graphical models to investigate dependencies among the features gathered in the surveys on dental caries. Materials and Methods - The data available in this analysis came from dental examinations conducted in children and from a questionnaire survey of their parents or guardians. The data represented 255 children aged between 36 and 48 months. Self-administered questionnaires contained 34 questions of socioeconomic and medical nature such as nutritional habits, wealth, or the level of education. The data included also the results of oral examination by a dentist. We applied the Bayesian network modeling to construct a model by learning it from the collected data. The process of Bayesian network model building was assisted by a dental expert. Results - The model allows to identify probabilistic relationships among the variables and to indicate the most significant risk factors of dental caries in three-year-old children. The Bayesian network model analysis illustrates that cleaning teeth and falling asleep with a bottle are the most significant risk factors of dental caries development in three-year-old children, whereas socioeconomic factors have no significant impact on the condition of teeth. Conclusions - Our analysis results suggest that dietary and oral hygiene habits have the most significant impact on the occurrence of dental caries in three-year-olds.
Źródło:
Progress in Health Sciences; 2019, 1; 118-125
2083-1617
Pojawia się w:
Progress in Health Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A hybrid approach to dimension reduction in classification
Autorzy:
Krawczak, M.
Szkatuła, G.
Powiązania:
https://bibliotekanauki.pl/articles/206425.pdf
Data publikacji:
2011
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
data series
dimension reduction
envelopes
essential attributes
heteroassociation
machine learning from examples
decision rules
classification
Opis:
In this paper we introduce a hybrid approach to data series classification. The approach is based on the concept of aggregated upper and lower envelopes, and the principal components here called 'essential attributes', generated by multilayer neural networks. The essential attributes are represented by outputs of hidden layer neurons. Next, the real valued essential attributes are nominalized and symbolic data series representation is obtained. The symbolic representation is used to generate decision rules in the IF. . . THEN. . . form for data series classification. The approach reduces the dimension of data series. The efficiency of the approach was verified by considering numerical examples.
Źródło:
Control and Cybernetics; 2011, 40, 2; 527-551
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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