02737nam 2200613 a 450 991043787340332120200520144314.01-283-90917-01-4419-9878-010.1007/978-1-4419-9878-1(CKB)2670000000308607(EBL)1081693(OCoLC)819571506(SSID)ssj0000811768(PQKBManifestationID)11458917(PQKBTitleCode)TC0000811768(PQKBWorkID)10850599(PQKB)10498465(DE-He213)978-1-4419-9878-1(MiAaPQ)EBC1081693(PPN)168291568(EXLCZ)99267000000030860720121018d2013 uy 0engur|n|---|||||txtccrRobust data mining /Petros Xanthopoulos, Panos M. Pardalos, Theodore B. Trafalis1st ed. 2013.New York Springer20131 online resource (66 p.)SpringerBriefs in optimization,2190-8354Description based upon print version of record.1-4419-9877-2 Includes bibliographical references.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.SpringerBriefs in Optimization,2190-8354Data miningRobust optimizationData mining.Robust optimization.006.312Xanthopoulos Petros1064749Pardalos P. M(Panos M.),1954-318341Trafalis Theodore B1751789MiAaPQMiAaPQMiAaPQBOOK9910437873403321Robust data mining4186904UNINA