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PL
EN
BibTeX
PN-ISO 690:2012
Chicago
Chicago (Author-Date)
Harvard
ACS
ACS (no art. title)
IEEE
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Journal
Śląski Przegląd Statystyczny
2020
|
18 (24)
| 241-248
Article title
Machine learning methods for classification problems
Authors
Groenitz Heiko
Content
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Title variants
Languages of publication
EN
Abstracts
Keywords
EN
algorithm
credit analysis
pattern recognition
random forest
support vector machine
Publisher
Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Journal
Śląski Przegląd Statystyczny
Year
2020
Issue
18 (24)
Pages
241-248
Physical description
Contributors
author
Groenitz Heiko
groenitz@staff.uni-marburg.de
Philipps University of Marburg, Germany
References
Breiman L. (2001), Random forests. Machine Learning Journal, 45, pp. 5-32.
Breiman L., Friedman J.H., Olshen R.A., Stone C.J. (1984), Classification and regression trees, Chapman & Hall/CRC, Boca Raton.
Cortes C., Vapnik V.N. (1995), Support-vector networks, Machine Learning, 20, pp. 273-297.
Hamel L. (2009), Knowledge Discovery with Support Vector Machines. John Wiley & Sons, Hoboken.
Jobson J.D. (1992), Applied Multivariate Data Analysis – Volume II: Categorical and Multivariate Methods, Springer, New York.
Lantz B. (2015), Machine Learning with R. Packt Publishing, Birmingham.
Moguerza J.M., Munoz A. (2006), Support vector machines with applications, Statistical Science, 21, pp. 322-336.
Pathak M.A. (2014), Beginning Data Science with R. Springer, Cham.
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.desklight-9711ed22-59be-4f5d-becc-a4e6789d35e9
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