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＜E-Book＞
Robust Data Mining / by Petros Xanthopoulos, Panos M. Pardalos, Theodore B. Trafalis
(SpringerBriefs in Optimization. ISSN:2191575X)

Edition	1st ed. 2013.
Publisher	(New York, NY : Springer New York : Imprint: Springer)
Year	2013
Language	English
Size	XII, 59 p. 6 illus : online resource
Authors	*Xanthopoulos, Petros author Pardalos, Panos M author Trafalis, Theodore B author SpringerLink (Online service)
Subjects	LCSH:Mathematical optimization LCSH:Data mining LCSH:Software engineering FREE:Optimization FREE:Data Mining and Knowledge Discovery FREE:Software Engineering
Notes	1. Introduction -- 2. Least Squares Problems -- 3. Principal Component Analysis -- 4. Linear Discriminant Analysis -- 5. Support Vector Machines -- 6. Conclusion Data uncertainty is a concept closely related with most real life applications that involve data collection and interpretation. Examples can be found in data acquired with biomedical instruments or other experimental techniques. Integration of robust optimization in the existing data mining techniques aim to create new algorithms resilient to error and noise. This work encapsulates all the latest applications of robust optimization in data mining. This brief contains an overview of the rapidly growing field of robust data mining research field and presents  the most well known machine learning algorithms, their robust counterpart formulations and algorithms for attacking these problems. This brief will appeal to theoreticians and data miners working in this field HTTP:URL=https://doi.org/10.1007/978-1-4419-9878-1

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