classification graffiti digits using von al fatlawi ali (6 Ergebnisse)

Paperback. Zustand: Brand New. 96 pages. 8.66x5.91x0.22 inches. In Stock.

Taschenbuch. Zustand: Neu. Classification of Graffiti digits by using Computational Intelligence | Several architectures and techniques to optimize the performance of the Neural Networks in the Pattern Recognition. | Ali H. Al-Fatlawi | Taschenbuch | 96 S. | Englisch | 2017 | Noor Publishing | EAN 9783330969360 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 ( Elektronisches Buch), 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. 96 pp. Englisch.

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Al-Fatlawi Ali H.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.

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 ( Elektronisches Buch), 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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 96 pp. Englisch.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 ( Elektronisches Buch), 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.