DocumentCode
1979805
Title
Using statistical learning theory for modeling the uncertainty in business and engineering systems: a qualitative introduction
Author
Guergachi, A. Aziz
Author_Institution
Sch. of Inf. Technol. Manage., Ryerson Polytech. Inst. Univ, Toronto, Ont., Canada
Volume
1
fYear
2001
fDate
2001
Firstpage
423
Abstract
Presents a qualitative introduction and justification of the application of statistical learning theory to uncertainty modeling in business and engineering systems. Using simple mathematical tools and metaphorical images, the main variables that govern the uncertainty in a physical system are defined. A general expression of uncertainty models is then obtained. The structure of this expression is the same as that of the uncertainty models that have been developed by rigorously applying the results of statistical learning theory
Keywords
commerce; corporate modelling; engineering; learning (artificial intelligence); statistics; uncertain systems; business systems; engineering systems; mathematical tools; metaphorical images; physical systems; statistical learning theory; uncertainty modeling; Engineering management; Filtration; Human resource management; Management training; Mathematical model; Power system management; Power system modeling; Statistical learning; Systems engineering and theory; Uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man, and Cybernetics, 2001 IEEE International Conference on
Conference_Location
Tucson, AZ
ISSN
1062-922X
Print_ISBN
0-7803-7087-2
Type
conf
DOI
10.1109/ICSMC.2001.969849
Filename
969849
Link To Document