Multiple Classifier Systems [electronic resource] : 11th International Workshop, MCS 2013, Nanjing, China, May 15-17, 2013. Proceedings / edited by Zhi-Hua Zhou, Fabio Roli, Josef Kittler.

By: Zhou, Zhi-Hua [editor.]Contributor(s): Roli, Fabio [editor.] | Kittler, Josef [editor.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Lecture Notes in Computer Science: 7872Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Description: XI, 400 p. 106 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783642380679Subject(s): Computer science | Data mining | Information storage and retrieval systems | Computer vision | Optical pattern recognition | Computer Science | Data Mining and Knowledge Discovery | Pattern Recognition | Image Processing and Computer Vision | Information Storage and RetrievalAdditional physical formats: Printed edition:: No titleDDC classification: 006.312 LOC classification: QA76.9.D343Online resources: Click here to access online
Contents:
Multiple classifier systems and ensemble methods -- Pattern recognition -- Machine learning -- Neural network -- Data mining -- Statistics.
In: Springer eBooksSummary: This book constitutes the refereed proceedings of the 11th International Workshop on Multiple Classifier Systems, MCS 2013, held in Nanjing, China, in May 2013. The 34 revised papers presented together with two invited papers were carefully reviewed and selected from 59 submissions. The papers address issues in multiple classifier systems and ensemble methods, including pattern recognition, machine learning, neural network, data mining and statistics.
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Multiple classifier systems and ensemble methods -- Pattern recognition -- Machine learning -- Neural network -- Data mining -- Statistics.

This book constitutes the refereed proceedings of the 11th International Workshop on Multiple Classifier Systems, MCS 2013, held in Nanjing, China, in May 2013. The 34 revised papers presented together with two invited papers were carefully reviewed and selected from 59 submissions. The papers address issues in multiple classifier systems and ensemble methods, including pattern recognition, machine learning, neural network, data mining and statistics.

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