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Cover: 9783330969360 | Classification of Graffiti digits by using Computational Intelligence
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Classification of Graffiti digits by using Computational Intelligence
Several architectures and techniques to optimize the performance of the Neural Networks in the Pattern Recognition.
Taschenbuch von Ali H. Al-Fatlawi
Sprache: Englisch

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Lieferzeit 4-7 Werktage

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Beschreibung
The technological advances and the massive flood of papers have motivated many researchers and companies to innovate new methods and technologies. They build automatic readers to recognize handwritten documents. In particular, handwriting recognition is very useful technology to support applications like electronic books (eBooks), postcode readers (that sort the mail in post offices), and some bank¿s applications. This book proposed systems to discriminate handwritten graffiti digits and some commands with different architectures and abilities. It introduced three classifiers, namely single neural network (SNN) classifier, parallel neural networks (PNN) classifier and tree-structured (TS) classifier. The three classifiers have been designed through adopting feed-forward neural networks. The back-propagation algorithm has been used to optimize the network¿s parameters (connection weights). Several architectures are applied and examined to present a comparative study about the three systems from different perspectives.
The technological advances and the massive flood of papers have motivated many researchers and companies to innovate new methods and technologies. They build automatic readers to recognize handwritten documents. In particular, handwriting recognition is very useful technology to support applications like electronic books (eBooks), postcode readers (that sort the mail in post offices), and some bank¿s applications. This book proposed systems to discriminate handwritten graffiti digits and some commands with different architectures and abilities. It introduced three classifiers, namely single neural network (SNN) classifier, parallel neural networks (PNN) classifier and tree-structured (TS) classifier. The three classifiers have been designed through adopting feed-forward neural networks. The back-propagation algorithm has been used to optimize the network¿s parameters (connection weights). Several architectures are applied and examined to present a comparative study about the three systems from different perspectives.
Über den Autor
Ali Al-Fatlawi is a researcher in the Information Technology Research and Development Center at University of Kufa since 2009. He has a Master degree from the University of Technology, Sydney (Australia) in field of the Computer Control Engineering. His works focus on investigating technologies and algorithms related to the machine learning.
Details
Erscheinungsjahr: 2017
Genre: Informatik, Mathematik, Medizin, Naturwissenschaften, Technik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Inhalt: 96 S.
ISBN-13: 9783330969360
ISBN-10: 3330969369
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Al-Fatlawi, Ali H.
Hersteller: Noor Publishing
Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, D-22848 Norderstedt, info@bod.de
Maße: 220 x 150 x 6 mm
Von/Mit: Ali H. Al-Fatlawi
Erscheinungsdatum: 15.05.2017
Gewicht: 0,161 kg
Artikel-ID: 109197741
Über den Autor
Ali Al-Fatlawi is a researcher in the Information Technology Research and Development Center at University of Kufa since 2009. He has a Master degree from the University of Technology, Sydney (Australia) in field of the Computer Control Engineering. His works focus on investigating technologies and algorithms related to the machine learning.
Details
Erscheinungsjahr: 2017
Genre: Informatik, Mathematik, Medizin, Naturwissenschaften, Technik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Inhalt: 96 S.
ISBN-13: 9783330969360
ISBN-10: 3330969369
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Al-Fatlawi, Ali H.
Hersteller: Noor Publishing
Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, D-22848 Norderstedt, info@bod.de
Maße: 220 x 150 x 6 mm
Von/Mit: Ali H. Al-Fatlawi
Erscheinungsdatum: 15.05.2017
Gewicht: 0,161 kg
Artikel-ID: 109197741
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