Classification of Graffiti digits by using Computational Intelligence : Several architectures and techniques to optimize the performance of the Neural Networks in the Pattern Recognition.
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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. (2017)
DE PB NWISBN: 9783330969360 bzw. 3330969369, in Deutsch, Noor Publishing Jun 2017, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, Germany.
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 (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. 96 pp. Englisch.
Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, Germany.
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 (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. 96 pp. Englisch.
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Symbolbild
Classification of Graffiti digits by using Computational Intelligence
DE NWISBN: 9783330969360 bzw. 3330969369, in Deutsch, neu.
Lieferung aus: Deutschland, Lieferzeit: 7 Tage.
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.
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Classification of Graffiti digits by using Computational Intelligence
~EN NW ABISBN: 9783330969360 bzw. 3330969369, vermutlich in Englisch, neu, Hörbuch.
Lieferung aus: Deutschland, Lieferzeit: 5 Tage.
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.
4
Symbolbild
Classification of Graffiti digits by using Computational Intelligence
DE PB NWISBN: 9783330969360 bzw. 3330969369, in Deutsch, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, European-Media-Service Mannheim [1048135], Mannheim, Germany.
Publisher/Verlag: Noor Publishing | Several architectures and techniques to optimize the performance of the Neural Networks in the Pattern Recognition. | 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. | Format: Paperback | Language/Sprache: english | 96 pp.
Von Händler/Antiquariat, European-Media-Service Mannheim [1048135], Mannheim, Germany.
Publisher/Verlag: Noor Publishing | Several architectures and techniques to optimize the performance of the Neural Networks in the Pattern Recognition. | 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. | Format: Paperback | Language/Sprache: english | 96 pp.
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Classification of Graffiti digits by using Computational Intelligence (2017)
DE PB NWISBN: 9783330969360 bzw. 3330969369, in Deutsch, 96 Seiten, Noor Publishing, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkosten nach: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, Buchhandlung Hoffmann, [3174608].
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 (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. 09.06.2017, Taschenbuch, Neuware, 220x150x6 mm, 159g, 96, Internationaler Versand, offene Rechnung (Vorkasse vorbehalten), sofortueberweisung.de, Selbstabholung und Barzahlung, Skrill/Moneybookers, PayPal, Lastschrift, Banküberweisung.
Von Händler/Antiquariat, Buchhandlung Hoffmann, [3174608].
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 (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. 09.06.2017, Taschenbuch, Neuware, 220x150x6 mm, 159g, 96, Internationaler Versand, offene Rechnung (Vorkasse vorbehalten), sofortueberweisung.de, Selbstabholung und Barzahlung, Skrill/Moneybookers, PayPal, Lastschrift, Banküberweisung.
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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. (2017)
EN PB NWISBN: 9783330969360 bzw. 3330969369, in Englisch, 96 Seiten, Noor Publishing, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandfertig in 1 - 2 Werktagen, Versandkostenfrei.
Von Händler/Antiquariat, dodax-shop-eu.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Von Händler/Antiquariat, dodax-shop-eu.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
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