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Robust Data Mining
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 techniqu...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Springer New York : Imprint: Springer,
2013.
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| Edición: | 1st ed. 2013. |
| Colección: | SpringerBriefs in Optimization,
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| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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|---|---|---|---|
| 001 | 978-1-4419-9878-1 | ||
| 003 | DE-He213 | ||
| 005 | 20220119202731.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 121120s2013 xxu| s |||| 0|eng d | ||
| 020 | |a 9781441998781 |9 978-1-4419-9878-1 | ||
| 024 | 7 | |a 10.1007/978-1-4419-9878-1 |2 doi | |
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| 100 | 1 | |a Xanthopoulos, Petros. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Robust Data Mining |h [electronic resource] / |c by Petros Xanthopoulos, Panos M. Pardalos, Theodore B. Trafalis. |
| 250 | |a 1st ed. 2013. | ||
| 264 | 1 | |a New York, NY : |b Springer New York : |b Imprint: Springer, |c 2013. | |
| 300 | |a XII, 59 p. 6 illus. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a SpringerBriefs in Optimization, |x 2191-575X | |
| 505 | 0 | |a 1. Introduction -- 2. Least Squares Problems -- 3. Principal Component Analysis -- 4. Linear Discriminant Analysis -- 5. Support Vector Machines -- 6. Conclusion. | |
| 520 | |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. | ||
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Software engineering. | |
| 650 | 1 | 4 | |a Optimization. |
| 650 | 2 | 4 | |a Data Mining and Knowledge Discovery. |
| 650 | 2 | 4 | |a Software Engineering. |
| 700 | 1 | |a Pardalos, Panos M. |e author. |0 (orcid)0000-0003-2824-101X |1 https://orcid.org/0000-0003-2824-101X |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Trafalis, Theodore B. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9781441998798 |
| 776 | 0 | 8 | |i Printed edition: |z 9781441998774 |
| 830 | 0 | |a SpringerBriefs in Optimization, |x 2191-575X | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-1-4419-9878-1 |z Texto Completo |
| 912 | |a ZDB-2-SMA | ||
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| 950 | |a Mathematics and Statistics (SpringerNature-11649) | ||
| 950 | |a Mathematics and Statistics (R0) (SpringerNature-43713) | ||