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Arabic and Chinese Handwriting Recognition, David Doermann; Stefan Jaeger


Варианты приобретения
Цена: 65210.00T
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Наличие: Поставка под заказ.  Есть в наличии на складе поставщика.
Склад Америка: 217 шт.  
При оформлении заказа до: 2025-07-28
Ориентировочная дата поставки: Август-начало Сентября
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Автор: David Doermann; Stefan Jaeger
Название:  Arabic and Chinese Handwriting Recognition
ISBN: 9783540781981
Издательство: Springer
Классификация:

ISBN-10: 3540781986
Обложка/Формат: Paperback
Страницы: 287
Вес: 0.46 кг.
Дата издания: 2008
Серия: Lecture Notes in Computer Science
Язык: English
Иллюстрации: Illustrations
Размер: 157 x 235 x 20
Читательская аудитория: Postgraduate, research & scholarly
Подзаголовок: Summit, sach 2006, college park, md, usa, september 27-28, 2006, selected papers
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: This book contains the proceedings of the Summit on Arabic and Chinese Handwriting Recognition. It features papers on Arabic handwriting, a survey of the language and techniques used in recognition as well as the current research in Chinese handwriting.

Pattern Recognition and Machine Learning

Автор: Christopher M. Bishop
Название: Pattern Recognition and Machine Learning
ISBN: 0387310738 ISBN-13(EAN): 9780387310732
Издательство: Springer
Рейтинг:
Цена: 79190.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Handwriting Recognition / Soft Computing and Probabilistic Approaches

Автор: Liu Zhi-Qiang, Cai Jin-Hai, Buse Richard
Название: Handwriting Recognition / Soft Computing and Probabilistic Approaches
ISBN: 3540401776 ISBN-13(EAN): 9783540401773
Издательство: Springer
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Цена: 65210.00 T
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Описание: This book takes a fresh look at the problem of unconstrained handwriting recognition and introduces the reader to new techniques for the recognition of written words and characters using statistical and soft computing approaches. The types of uncertainties and variations present in handwriting data are discussed in detail. The book presents several algorithms that use modified hidden Markov models and Markov random field models to simulate the handwriting data statistically and structurally in a single framework. The book explores methods that use fuzzy logic and fuzzy sets for handwriting recognition. The effectiveness of these techniques is demonstrated through extensive experimental results and real handwritten characters and words.

Neural Networks for Pattern Recognition

Автор: Bishop, Christopher M.
Название: Neural Networks for Pattern Recognition
ISBN: 0198538642 ISBN-13(EAN): 9780198538646
Издательство: Oxford Academ
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Цена: 92930.00 T
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Описание: This book is the first to provide a comprehensive account of neural networks from a statistical perspective. Its emphasis is on pattern recognition, which currently represents the area of greatest applicability for neural networks. By focusing on pattern recognition, the book provides a much more extensive treatment of many topics than is available in earlier books.

Correlation Pattern Recognition

Автор: Kumar
Название: Correlation Pattern Recognition
ISBN: 0521153484 ISBN-13(EAN): 9780521153485
Издательство: Cambridge Academ
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Цена: 57030.00 T
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Описание: This book provides a needed review of the diverse background material needed for correlation pattern recognition, developing the signal processing theory, the pattern recognition metrics, and the practical application know-how from basic premises. Includes case studies of interest, such as face and fingerprint recognition. For graduate students and practitioners.

Pattern Recognition and Neural Networks

Автор: Brian D. Ripley
Название: Pattern Recognition and Neural Networks
ISBN: 0521717701 ISBN-13(EAN): 9780521717700
Издательство: Cambridge Academ
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Цена: 51750.00 T
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Описание: This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. Valuable advice is included on both theory and applications, while case studies based on real data sets help readers develop their understanding. All data sets are available from www.stats.ox.ac.uk/~ripley/PRbook/


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