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130626 ||| eng |
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|a 9781441998781
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|a Xanthopoulos, Petros
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|a Robust Data Mining
|h Elektronische Ressource
|c by Petros Xanthopoulos, Panos M. Pardalos, Theodore B. Trafalis
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|a 1st ed. 2013
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260 |
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|a New York, NY
|b Springer New York
|c 2013, 2013
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300 |
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|a XII, 59 p. 6 illus
|b online resource
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|a 1. Introduction -- 2. Least Squares Problems -- 3. Principal Component Analysis -- 4. Linear Discriminant Analysis -- 5. Support Vector Machines -- 6. Conclusion
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653 |
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|a Software engineering
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653 |
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|a Optimization
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653 |
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|a Software Engineering
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653 |
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|a Data mining
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653 |
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|a Data Mining and Knowledge Discovery
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653 |
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|a Mathematical optimization
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|a Pardalos, Panos M.
|e [author]
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|a Trafalis, Theodore B.
|e [author]
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|a eng
|2 ISO 639-2
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|b Springer
|a Springer eBooks 2005-
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|a SpringerBriefs in Optimization
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|a 10.1007/978-1-4419-9878-1
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|u https://doi.org/10.1007/978-1-4419-9878-1?nosfx=y
|x Verlag
|3 Volltext
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|a 519.6
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|a 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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