Robust Data Mining

Nonfiction, Science & Nature, Mathematics, Applied, Computers, Database Management
Cover of the book Robust Data Mining by Petros Xanthopoulos, Theodore B. Trafalis, Panos M. Pardalos, Springer New York
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Author: Petros Xanthopoulos, Theodore B. Trafalis, Panos M. Pardalos ISBN: 9781441998781
Publisher: Springer New York Publication: November 28, 2012
Imprint: Springer Language: English
Author: Petros Xanthopoulos, Theodore B. Trafalis, Panos M. Pardalos
ISBN: 9781441998781
Publisher: Springer New York
Publication: November 28, 2012
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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