Idioma: Inglés
Publicado por Scholars' Press Jul 2019, 2019
ISBN 10: 6138839145 ISBN 13: 9786138839149
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware -This book critically reviews the various soft computing techniques employed in handwriting recognition and presents recognition accuracy achievements available in the literature. With advancements in the areas of artificial intelligence and machine learning, the expectation and challenges in handwriting recognition have become more and more demanding. The focus of this book is to explore the various steps involved in a handwriting recognition system such as pre-processing, feature extraction, feature selection and classification. Soft computing techniques such as neural network, fuzzy logic, genetic algorithm and neuro-fuzzy are applied in the recognition process. Some attempts based on hybrid feature extraction, GA based feature subset selection, ranking based feature selection methods, and optimization of learning parameters are also employed for further improvement in the recognition accuracy. 208 pp. Englisch.
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Añadir al carritoCondición: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | This book critically reviews the various soft computing techniques employed in handwriting recognition and presents recognition accuracy achievements available in the literature. With advancements in the areas of artificial intelligence and machine learning, the expectation and challenges in handwriting recognition have become more and more demanding. The focus of this book is to explore the various steps involved in a handwriting recognition system such as pre-processing, feature extraction, feature selection and classification. Soft computing techniques such as neural network, fuzzy logic, genetic algorithm and neuro-fuzzy are applied in the recognition process. Some attempts based on hybrid feature extraction, GA based feature subset selection, ranking based feature selection methods, and optimization of learning parameters are also employed for further improvement in the recognition accuracy.
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Añadir al carritoCondición: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This book critically reviews the various soft computing techniques employed in handwriting recognition and presents recognition accuracy achievements available in the literature. With advancements in the areas of artificial intelligence and machine learning, the expectation and challenges in handwriting recognition have become more and more demanding. The focus of this book is to explore the various steps involved in a handwriting recognition system such as pre-processing, feature extraction, feature selection and classification. Soft computing techniques such as neural network, fuzzy logic, genetic algorithm and neuro-fuzzy are applied in the recognition process. Some attempts based on hybrid feature extraction, GA based feature subset selection, ranking based feature selection methods, and optimization of learning parameters are also employed for further improvement in the recognition accuracy.
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Añadir al carritoTaschenbuch. Condición: Neu. Optimal Handwriting Recognition Using Soft Computing Techniques | Design and Implementation | Savita Ahlawat | Taschenbuch | 208 S. | Englisch | 2019 | Scholars' Press | EAN 9786138839149 | Verantwortliche 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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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ahlawat SavitaDr Savita Ahlawat is currently working as a Reader (CSE Dept.) at MSIT, New Delhi. She has done B.E., M.Tech.(IT) & Ph.D. (CSE) and holds 15 years of teaching experience. She has published around 25 papers in internatio.
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Idioma: Inglés
Publicado por Scholars' Press Jul 2019, 2019
ISBN 10: 6138839145 ISBN 13: 9786138839149
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
EUR 79,90
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book critically reviews the various soft computing techniques employed in handwriting recognition and presents recognition accuracy achievements available in the literature. With advancements in the areas of artificial intelligence and machine learning, the expectation and challenges in handwriting recognition have become more and more demanding. The focus of this book is to explore the various steps involved in a handwriting recognition system such as pre-processing, feature extraction, feature selection and classification. Soft computing techniques such as neural network, fuzzy logic, genetic algorithm and neuro-fuzzy are applied in the recognition process. Some attempts based on hybrid feature extraction, GA based feature subset selection, ranking based feature selection methods, and optimization of learning parameters are also employed for further improvement in the recognition accuracy.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 208 pp. Englisch.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book critically reviews the various soft computing techniques employed in handwriting recognition and presents recognition accuracy achievements available in the literature. With advancements in the areas of artificial intelligence and machine learning, the expectation and challenges in handwriting recognition have become more and more demanding. The focus of this book is to explore the various steps involved in a handwriting recognition system such as pre-processing, feature extraction, feature selection and classification. Soft computing techniques such as neural network, fuzzy logic, genetic algorithm and neuro-fuzzy are applied in the recognition process. Some attempts based on hybrid feature extraction, GA based feature subset selection, ranking based feature selection methods, and optimization of learning parameters are also employed for further improvement in the recognition accuracy.