Multiple Classifier Systems [electronic resource] : 10th International Workshop, MCS 2011, Naples, Italy, June 15-17, 2011. Proceedings / edited by Carlo Sansone, Josef Kittler, Fabio Roli.
Contributor(s): Sansone, Carlo [editor.]
| Kittler, Josef [editor.] | Roli, Fabio [editor.] | SpringerLink (Online service).
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BookSeries: Image Processing, Computer Vision, Pattern Recognition, and Graphics: 6713Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2011Edition: 1st ed. 2011.Description: XII, 372 p. 84 illus., 48 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642215575.Subject(s): Application software | Pattern recognition systems | Algorithms | Computer science | Database management | Data mining | Computer and Information Systems Applications | Automated Pattern Recognition | Algorithms | Theory of Computation | Database Management | Data Mining and Knowledge DiscoveryAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 005.3 Online resources: Click here to access online
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Springer Nature eBookSummary: This book constitutes the refereed proceedings of the 10th International Workshop on Multiple Classifier Systems, MCS 2011, held in Naples, Italy, in June 2011. The 36 revised papers presented together with two invited papers were carefully reviewed and selected from more than 50 submissions. The contributions are organized into sessions dealing with classifier ensembles; trees and forests; one-class classifiers; multiple kernels; classifier selection; sequential combination; ECOC; diversity; clustering; biometrics; and computer security.
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This book constitutes the refereed proceedings of the 10th International Workshop on Multiple Classifier Systems, MCS 2011, held in Naples, Italy, in June 2011. The 36 revised papers presented together with two invited papers were carefully reviewed and selected from more than 50 submissions. The contributions are organized into sessions dealing with classifier ensembles; trees and forests; one-class classifiers; multiple kernels; classifier selection; sequential combination; ECOC; diversity; clustering; biometrics; and computer security.
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