Robust Data Mining
By: by Petros Xanthopoulos, Panos M. Pardalos, Theodore B. Trafalis
Contributors: Pardalos, P. M. (Panos M.), 1954-; Trafalis, Theodore B.; SpringerLink (Online service)
Series: SpringerBriefs in Optimization
Publisher: Springer New York; Imprint: Springer
Published: 2013
Place of publication: New York, NY
Language: eng
Format: [electronic resource] /
Number of pages: 59
ISBN-13: 9781441998781
Subjects: Software Engineering/Programming and Operating Systems; Optimization; Data Mining and Knowledge Discovery; Software engineering; Data mining; Mathematics; Mathematical optimization
Description: <p>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.</p><p>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. </p><p>This brief will appeal to theoreticians and data miners working in this field.</p><p>
Table of contents: title: 1. Introduction; title: 2. Least Squares Problems; title: 3. Principal Component Analysis; title: 4. Linear Discriminant Analysis; title: 5. Support Vector Machines; title: 6. Conclusion.