9786139914357 - optimized two-stage ensemble model for license plate recognition: memetically optimized two-stage fuzzy support vector machine ensemble model for license plate recognition von samma, hussein; mohamad-saleh, junita (6 Ergebnisse)
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Paperback. Zustand: Brand New. 132 pages. 8.66x5.91x0.30 inches. In Stock.
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Taschenbuch. Zustand: Neu. Optimized Two-Stage Ensemble Model for License Plate Recognition | Memetically Optimized Two-Stage Fuzzy Support Vector Machine Ensemble Model for License Plate Recognition | Hussein Samma (u. a.) | Taschenbuch | 132 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139914357 | Verantwor…tliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Pattern recognition models play an important role in many real-world applications such as text detection and object recognition. Numerous methodologies including Computational Intelligence (CI) models have been developed in the litera…ture to tackle image-based pattern recognition problems. Focused on CI models, this research presents efficient Particle Swarm Optimization (PSO)-based models and their application to license plate recognition. Firstly, a new Reinforcement Learning-based Memetic Particle Swarm Optimization (RLMPSO) model is introduced. Then, RLMPSO is integrated with the Fuzzy Support Vector Machine (FSVM) to formulate an efficient two-stage RLMPSO-FSVM model. Specifically, two-stage RLMPSO-FSVM comprises an ensemble of linear FSVM classifiers that are constructed using RLMPSO to perform parameter tuning, feature selection, as well as training sample selection. Finally, the proposed two-stage RLMPSO-FSVM model is applied to a real-world Malaysian vehicle license plate recognition (VLPR) task. 132 pp. Englisch.
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Samma HusseinDr. Hussein Samma has a B.S.c in computer engineering (Yarmouk University - Jordan), M.Sc. in computer engineering (Jordan University of Science and Technology, Jordan), and Ph.D. in computat…ional intelligence (Universi.
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Pattern recognition models play an important role in many real-world applications such as text detection and object recognition. Numerous methodologies including Computational Intelligence (CI) models have been developed in the literature… to tackle image-based pattern recognition problems. Focused on CI models, this research presents efficient Particle Swarm Optimization (PSO)-based models and their application to license plate recognition. Firstly, a new Reinforcement Learning-based Memetic Particle Swarm Optimization (RLMPSO) model is introduced. Then, RLMPSO is integrated with the Fuzzy Support Vector Machine (FSVM) to formulate an efficient two-stage RLMPSO-FSVM model. Specifically, two-stage RLMPSO-FSVM comprises an ensemble of linear FSVM classifiers that are constructed using RLMPSO to perform parameter tuning, feature selection, as well as training sample selection. Finally, the proposed two-stage RLMPSO-FSVM model is applied to a real-world Malaysian vehicle license plate recognition (VLPR) task.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 132 pp. Englisch.
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Pattern recognition models play an important role in many real-world applications such as text detection and object recognition. Numerous methodologies including Computational Intelligence (CI) models have been developed in the literature…to tackle image-based pattern recognition problems. Focused on CI models, this research presents efficient Particle Swarm Optimization (PSO)-based models and their application to license plate recognition. Firstly, a new Reinforcement Learning-based Memetic Particle Swarm Optimization (RLMPSO) model is introduced. Then, RLMPSO is integrated with the Fuzzy Support Vector Machine (FSVM) to formulate an efficient two-stage RLMPSO-FSVM model. Specifically, two-stage RLMPSO-FSVM comprises an ensemble of linear FSVM classifiers that are constructed using RLMPSO to perform parameter tuning, feature selection, as well as training sample selection. Finally, the proposed two-stage RLMPSO-FSVM model is applied to a real-world Malaysian vehicle license plate recognition (VLPR) task.
