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.

By: Sansone, Carlo [editor.]Contributor(s): Kittler, Josef [editor.] | Roli, Fabio [editor.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Lecture Notes in Computer Science: 6713Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011Description: XII, 372p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783642215575Subject(s): Computer science | Computer software | Database management | Data mining | Optical pattern recognition | Computer Science | Information Systems Applications (incl. Internet) | Pattern Recognition | Algorithm Analysis and Problem Complexity | Computation by Abstract Devices | Database Management | Data Mining and Knowledge DiscoveryAdditional physical formats: Printed edition:: No titleDDC classification: 005.7 LOC classification: QA76.76.A65Online resources: Click here to access online In: Springer eBooksSummary: 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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