Biometric Authentication : A Machine Learning Approach Hardcover

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Como nuevo
Libro en perfecto estado y poco leído. La tapa no tiene desperfectos y si procede, con sobrecubierta para las tapas duras. Incluye todas las páginas sin arrugas ni roturas. El texto no está subrayado ni resaltado de forma alguna, y no hay anotaciones en los márgenes. Puede presentar marcas de identificación mínimas en la contraportada o las guardas. Muy poco usado. Consulta el anuncio del vendedor para obtener más información y la descripción de cualquier posible imperfección. Ver todas las definiciones de estadose abre en una nueva ventana o pestaña
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“The book is in like-new condition. I see no obvious flaws with it”
Subject Area
Mechanical Engineering
Book Title
Biometric Authentication : A Machine Learning Approach Hardcover
Educational Level
Adult & Further Education, High School
Level
Intermediate, Advanced, Business
Subject
Design, Technology
ISBN
9780131478244
Categoría

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Product Identifiers

Publisher
Prentice Hall PTR
ISBN-10
0131478249
ISBN-13
9780131478244
eBay Product ID (ePID)
30767666

Product Key Features

Number of Pages
496 Pages
Publication Name
Biometric Authentication : a Machine Learning Approach
Language
English
Subject
General, Computer Vision & Pattern Recognition
Publication Year
2004
Type
Textbook
Subject Area
Law, Computers
Author
M. W. Mak, Sun-Yuang Kung, S. H. Lin
Series
Prentice Hall Information and System Sciences Ser.
Format
Hardcover

Dimensions

Item Height
1.5 in
Item Weight
39.4 Oz
Item Length
9.3 in
Item Width
6.9 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
2004-012612
Dewey Edition
22
Illustrated
Yes
Dewey Decimal
621.389/28
Table Of Content
Machine learning: driving significant improvements in biometric performance As they improve, biometric authentication systems are becoming increasingly indispensable for protecting life and property. This book introduces powerful machine learning techniques that significantly improve biometric performance in a broad spectrum of application domains. Three leading researchers bridge the gap between research, design, and deployment, introducing key algorithms as well as practical implementation techniques. They demonstrate how to construct robust information processing systems for biometric authentication in both face and voice recognition systems, and to support data fusion in multimodal systems. Coverage includes: How machine learning approaches differ from conventional template matching Theoretical pillars of machine learning for complex pattern recognition and classification Expectation-maximization (EM) algorithms, Fisher's Linear Discriminant Analysis (LDA), and support vector machines (SVM) Multi-layer learning models and back-propagation (BP) algorithms Probabilistic decision-based neural networks (PDNNs) for face biometrics Flexible structural frameworks for incorporating machine learning subsystems in biometric applications Hierarchical mixture of experts and inter-class learning strategies based on class-based modular networks Multi-cue data fusion techniques that integrate face and voice recognition Application case studies The biometrics industry is expected to grow by 600% in the next four years. Machine learning will help that drive that growth. Whether you're an engineer, scientist, developer, or integrator, this book will help you make the most of it.
Synopsis
Machine learning: driving significant improvements in biometric performance As they improve, biometric authentication systems are becoming increasingly indispensable for protecting life and property. This book introduces powerful machine learning techniques that significantly improve biometric performance in a broad spectrum of application domains. Three leading researchers bridge the gap between research, design, and deployment, introducing key algorithms as well as practical implementation techniques. They demonstrate how to construct robust information processing systems for biometric authentication in both face and voice recognition systems, and to support data fusion in multimodal systems. Coverage includes: How machine learning approaches differ from conventional template matching Theoretical pillars of machine learning for complex pattern recognition and classification Expectation-maximization (EM) algorithms..., A breakthrough approach to improving biometrics performance Constructing robust information processing systems for face and voice recognition Supporting high-performance data fusion in multimodal systems Algorithms, implementation techniques, and application examples Machine learning: driving significant improvements in biometric performance As they improve, biometric authentication systems are becoming increasingly indispensable for protecting life and property. This book introduces powerful machine learning techniques that significantly improve biometric performance in a broad spectrum of application domains. Three leading researchers bridge the gap between research, design, and deployment, introducing key algorithms as well as practical implementation techniques. They demonstrate how to construct robust information processing systems for biometric authentication in both face and voice recognition systems, and to support data fusion in multimodal systems. Coverage includes: How machine learning approaches differ from conventional template matching Theoretical pillars of machine learning for complex pattern recognition and classification Expectation-maximization (EM) algorithms and support vector machines (SVM) Multi-layer learning models and back-propagation (BP) algorithms Probabilistic decision-based neural networks (PDNNs) for face biometrics Flexible structural frameworks for incorporating machine learning subsystems in biometric applications Hierarchical mixture of experts and inter-class learning strategies based on class-based modular networks Multi-cue data fusion techniques that integrate face and voice recognition Application case studies
LC Classification Number
TK7882.P3K84 2004

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