Statistical approach neural networks (25 resultados)
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Condición: Like New. Used - Like New. Book is new and unread but may have minor shelf wear. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.
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Taschenbuch. Condición: Neu. Contribution To The Solution Of Fingerprint Identification Problem | Fingerprints Classification and Identification: A Statistical, Structural and Artificial Neural Networks Approach | Khaled Nagati | Taschenbuch | Englisch | LAP Lambert Academic Publishing | EAN 9783659106231 | Verantwortliche Perso…n für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Condición: New. pp. xvii + 268 Illus.
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Condición: New. pp. xvii + 268.
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Condición: New. This book presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models, in a language that is familiar to practicing statisticians. Questions arise when statisticians are first confronted with such a model, and this book's aim is to provide thorou…gh answers. Series: Wiley Series in Computational Statistics. Num Pages: 288 pages, Illustrations. BIC Classification: UYQN. Category: (P) Professional & Vocational. Dimension: 236 x 162 x 19. Weight in Grams: 538. . 2007. 1st Edition. Hardcover. . . . .
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Paperback. Condición: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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hardcover. Condición: New. In shrink wrap. Looks like an interesting title.
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Hardcover. Condición: Brand New. 1st edition. 288 pages. 9.75x6.50x0.75 inches. In Stock.
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Condición: New. This book presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models, in a language that is familiar to practicing statisticians. Questions arise when statisticians are first confronted with such a model, and this book's aim is to provide thorou…gh answers. Series: Wiley Series in Computational Statistics. Num Pages: 288 pages, Illustrations. BIC Classification: UYQN. Category: (P) Professional & Vocational. Dimension: 236 x 162 x 19. Weight in Grams: 538. . 2007. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.
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Buch. Condición: Neu. Neuware - An accessible and up-to-date treatment featuring the connection between neural networks and statistics A Statistical Approach to Neural Networks for Pattern Recognition presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models.…This book aims to answer questions that arise when statisticians are first confronted with this type of model, such as: How robust is the model to outliers Could the model be made more robust Which points will have a high leverage What are good starting values for the fitting algorithm Thorough answers to these questions and many more are included, as well as worked examples and selected problems for the reader. Discussions on the use of MLP models with spatial and spectral data are also included. Further treatment of highly important principal aspects of the MLP are provided, such as the robustness of the model in the event of outlying or atypical data; the influence and sensitivity curves of the MLP; why the MLP is a fairly robust model; and modifications to make the MLP more robust. The author also provides clarification of several misconceptions that are prevalent in existing neural network literature. Throughout the book, the MLP model is extended in several directions to show that a statistical modeling approach can make valuable contributions, and further exploration for fitting MLP models is made possible via the R and S-PLUS® codes that are available on the book's related Web site. A Statistical Approach to Neural Networks for Pattern Recognition successfully connects logistic regression and linear discriminant analysis, thus making it a critical reference and self-study guide for students and professionals alike in the fields of mathematics, statistics, computer science, and electrical engineering.
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - A fingerprint is the feature pattern of one finger. It is believed with strong evidence that each fingerprint is unique. Each person has his own fingerprints with the permanent uniqueness. So fingerprints have being used for identificati…on and forensic investigation for a long time. This book explores new approaches for designing the various stages of fingerprint classification and identification system. Readers will see how to use models for: - Fingerprint ridges restoration. - Extraction of structural and statistical features of a fingerprint. - Supervised fingerprints classification based on an artificial neural network (ANN)that combines the structural and statistical features of a fingerprint. - Detection of the core point of a fingerprint using an ANN model. - Fingerprints score matching based on the comparison of the relational curves which join the detected minutiae of each fingerprint. Curve matching uses string matching and T-test to generate a list of fingerprint candidates. All topics in this book are organized to be easy to understand. The practical emphasis is reinforced with many illustrative examples for each stage of the fingerprint identification system.
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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Hardcover. Condición: Brand New. 1st edition. 288 pages. 9.75x6.50x0.75 inches. In Stock. This item is printed on demand.
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Hardback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 568.
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Hardcover. Condición: new. Hardcover. An accessible and up-to-date treatment featuring the connection between neural networks and statistics A Statistical Approach to Neural Networks for Pattern Recognition presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network mo…dels. This book aims to answer questions that arise when statisticians are first confronted with this type of model, such as: How robust is the model to outliers? Could the model be made more robust? Which points will have a high leverage? What are good starting values for the fitting algorithm? Thorough answers to these questions and many more are included, as well as worked examples and selected problems for the reader. Discussions on the use of MLP models with spatial and spectral data are also included. Further treatment of highly important principal aspects of the MLP are provided, such as the robustness of the model in the event of outlying or atypical data; the influence and sensitivity curves of the MLP; why the MLP is a fairly robust model; and modifications to make the MLP more robust. The author also provides clarification of several misconceptions that are prevalent in existing neural network literature. Throughout the book, the MLP model is extended in several directions to show that a statistical modeling approach can make valuable contributions, and further exploration for fitting MLP models is made possible via the R and S-PLUS codes that are available on the book's related Web site. A Statistical Approach to Neural Networks for Pattern Recognition successfully connects logistic regression and linear discriminant analysis, thus making it a critical reference and self-study guide for students and professionals alike in the fields of mathematics, statistics, computer science, and electrical engineering. This book presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models, in a language that is familiar to practicing statisticians. Questions arise when statisticians are first confronted with such a model, and this book's aim is to provide thorough answers. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
