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
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;
Conference_Titel :
Systems, Man, and Cybernetics, 2001 IEEE International Conference on
Conference_Location :
Tucson, AZ
Print_ISBN :
0-7803-7087-2
DOI :
10.1109/ICSMC.2001.969849