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Diversity-Based Hybrid Classifier Fusion - Rasheed, Sarbast
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Rasheed, Sarbast:
Diversity-Based Hybrid Classifier Fusion - Livres de poche

2008, ISBN: 3836435330, Lieferbar binnen 4-6 Wochen Frais d'envoiVersandkostenfrei innerhalb der BRD

ID: 9783836435338

Internationaler Buchtitel. In englischer Sprache. Verlag: VDM Verlag, Paperback, 216 Seiten, L=240mm, B=170mm, H=13mm, Gew.=418gr, [GR: 16790 - HC/Biologie/Sonstiges], Kartoniert/Broschiert, Klappentext: Electromyographic (EMG) signal analysis is the process of resolving a composite EMG signal into its constituent motor unit potential trains (classes) and it can be configured as a classification problem. An EMG signal detected by the tip of an inserted needle electrode is the superposition of the indivi­dual electrical contributions of the different motor units that are active, during a muscle contraction, and background interference. This book addresses the process of EMG signal decomposition by developing an interactive classification system, which uses multiple classifier fusion techniques in order to achieve improved classification performance. The developed system combines heterogeneous sets of base classifier ensembles of different kinds and employs both a one level classifier fusion scheme and a hybrid classifier fusion approach. Performance of the developed system was evaluated using synthetic simulated signals of known properties and real signals and compared with the performance of the constituent base classifiers. This book is directed toward graduate students and researchers in the area of electromyography and professionals in electromyography clinics. Electromyographic (EMG) signal analysis is the process of resolving a composite EMG signal into its constituent motor unit potential trains (classes) and it can be configured as a classification problem. An EMG signal detected by the tip of an inserted needle electrode is the superposition of the indivi­dual electrical contributions of the different motor units that are active, during a muscle contraction, and background interference. This book addresses the process of EMG signal decomposition by developing an interactive classification system, which uses multiple classifier fusion techniques in order to achieve improved classification performance. The developed system combines heterogeneous sets of base classifier ensembles of different kinds and employs both a one level classifier fusion scheme and a hybrid classifier fusion approach. Performance of the developed system was evaluated using synthetic simulated signals of known properties and real signals and compared with the performance of the constituent base classifiers. This book is directed toward graduate students and researchers in the area of electromyography and professionals in electromyography clinics.

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Diversity-Based Hybrid Classifier Fusion - Rasheed, Sarbast
Livre non disponible
(*)
Rasheed, Sarbast:
Diversity-Based Hybrid Classifier Fusion - Livres de poche

2008, ISBN: 3836435330, Lieferbar binnen 4-6 Wochen

ID: 9783836435338

Internationaler Buchtitel. In englischer Sprache. Verlag: VDM Verlag, Paperback, 216 Seiten, L=240mm, B=170mm, H=13mm, Gew.=418gr, [GR: 16790 - HC/Biologie/Sonstiges], Kartoniert/Broschiert, Klappentext: Electromyographic (EMG) signal analysis is the process of resolving a composite EMG signal into its constituent motor unit potential trains (classes) and it can be configured as a classification problem. An EMG signal detected by the tip of an inserted needle electrode is the superposition of the indivi­dual electrical contributions of the different motor units that are active, during a muscle contraction, and background interference. This book addresses the process of EMG signal decomposition by developing an interactive classification system, which uses multiple classifier fusion techniques in order to achieve improved classification performance. The developed system combines heterogeneous sets of base classifier ensembles of different kinds and employs both a one level classifier fusion scheme and a hybrid classifier fusion approach. Performance of the developed system was evaluated using synthetic simulated signals of known properties and real signals and compared with the performance of the constituent base classifiers. This book is directed toward graduate students and researchers in the area of electromyography and professionals in electromyography clinics.

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Diversity-Based Hybrid Classifier Fusion - Rasheed, Sarbast
Livre non disponible
(*)
Rasheed, Sarbast:
Diversity-Based Hybrid Classifier Fusion - Livres de poche

2008, ISBN: 9783836435338

[ED: Softcover], [PU: Vdm Verlag Dr. Müller], Electromyographic (EMG) signal analysis is the process of resolving a composite EMG signal into its constituent motor unit potential trains (classes) and it can be configuredas a classification problem. An EMG signal detected by the tip of an inserted needle electrode is the superposition of the individual electrical contributions of the different motor units that are active, during a muscle contraction, and background interference. This book addresses the process of EMG signal decomposition by developing an interactive classification system, which uses multiple classifier fusion techniques in order to achieve improved classification performance. The developed system combines heterogeneous sets of base classifier ensembles of different kinds and employs both a one level classifier fusion scheme and a hybrid classifier fusion approach. Performance of the developed system was evaluated using synthetic simulated signals of known properties and real signals and compared with the performance of the constituent base classifiers. This book is directed toward graduate students and researchers in the area of electromyography and professionals in electromyography clinics.2008. 216 p.Versandfertig in 3-5 Tagen, [SC: 0.00]

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(*) Livre non disponible signifie que le livre est actuellement pas disponible à l'une des plates-formes associées nous recherche.
Diversity-Based Hybrid Classifier Fusion - Rasheed, Sarbast
Livre non disponible
(*)
Rasheed, Sarbast:
Diversity-Based Hybrid Classifier Fusion - Livres de poche

2008, ISBN: 9783836435338

[ED: Softcover], [PU: Vdm Verlag Dr. Müller], Electromyographic (EMG) signal analysis is the process of resolving a composite EMG signal into its constituent motor unit potential trains (classes) and it can be configuredas a classification problem. An EMG signal detected by the tip of an inserted needle electrode is the superposition of the individual electrical contributions of the different motor units that are active, during a muscle contraction, and background interference. This book addresses the process of EMG signal decomposition by developing an interactive classification system, which uses multiple classifier fusion techniques in order to achieve improved classification performance. The developed system combines heterogeneous sets of base classifier ensembles of different kinds and employs both a one level classifier fusion scheme and a hybrid classifier fusion approach. Performance of the developed system was evaluated using synthetic simulated signals of known properties and real signals and compared with the performance of the constituent base classifiers. This book is directed toward graduate students and researchers in the area of electromyography and professionals in electromyography clinics. 2008. 216 p. Versandfertig in 3-5 Tagen

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Détails sur le livre
Diversity-Based Hybrid Classifier Fusion

Electromyographic (EMG) signal analysis is the process of resolving a composite EMG signal into its constituent motor unit potential trains (classes) and it can be configured as a classification problem. An EMG signal detected by the tip of an inserted needle electrode is the superposition of the indivi­dual electrical contributions of the different motor units that are active, during a muscle contraction, and background interference. This book addresses the process of EMG signal decomposition by developing an interactive classification system, which uses multiple classifier fusion techniques in order to achieve improved classification performance. The developed system combines heterogeneous sets of base classifier ensembles of different kinds and employs both a one level classifier fusion scheme and a hybrid classifier fusion approach. Performance of the developed system was evaluated using synthetic simulated signals of known properties and real signals and compared with the performance of the constituent base classifiers. This book is directed toward graduate students and researchers in the area of electromyography and professionals in electromyography clinics.

Informations détaillées sur le livre - Diversity-Based Hybrid Classifier Fusion


EAN (ISBN-13): 9783836435338
ISBN (ISBN-10): 3836435330
Livre de poche
Date de parution: 2008
Editeur: VDM Verlag
216 Pages
Poids: 0,418 kg
Langue: eng/Englisch

Livre dans la base de données depuis 29.01.2009 07:42:03
Livre trouvé récemment le 16.01.2012 20:44:11
ISBN/EAN: 3836435330

ISBN - Autres types d'écriture:
3-8364-3533-0, 978-3-8364-3533-8


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