Pattern Recognition Using Neural Networks

Inbunden, Engelska, 1997

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Pattern Regcognition with Neural Networks covers traditional linear pattern recognition and its nonlinear extension via neural networks from an algorithmic approach. The author has written a real-world practical "why-and-how" text that provides a refreshing contrast to competing texts' thoeretical appraoch and "pie-in-the-sky" claims. The text explores mulitple layered preceptrons and describes network types such as functional link, radial basis function, learning vector quantanization and self-organizing. The author also discusses recent clustering methods. This text is suitable for an advanced undergraduate course in pattern recognition or neural networks, and is also useful as a reference and a resource.

Produktinformation

Utgivningsdatum1997-02-20
Vikt1 020 g
FormatInbunden
SpråkEngelska
Antal sidor480
FörlagOxford University Press Inc
ISBN9780195079203

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Systemvetenskap och AI inom Data och it
Programmeringsböcker inom Data och it

Part I FUNDAMENTALS OF PATTERN RECOGNITION ; 0. Basic Concepts of Pattern Recognition ; 1. Decision Theoretic Algorithms ; 2. Structural Pattern Recognition ; Part II INTRODUCTORY NEURAL NETWORKS ; 3. Artificial Neural Network Structures ; 4. Supervised Training via Error Backpropogation: Derivations ; 5. Acceleration and Stabilization of Supervised Gradient Training of MLPs ; Part III ADVANCED FUNDAMENTALS OF NEURAL NETWORKS ; 6. Supervised Training via Strategic Search ; 7. Advances in Network Algorithms for Recognition ; 8. Using Hopfield Recurrent Neural Networks ; Part IV NEURAL, FEATURE, AND DATA ENGINEERING ; 9. Neural Engineering and Testing of FANNs ; 10. Feature and Data Engineering

This is a fairly comprehensive introduction to feedforward neutral networks...the book is accessible and would be well-suited to serve as a text for its intended audience Short Book Review Vol. 17 No. 3 '... makes its subject easy to understand by offering intuitive explanations and examples... lives up to its claim as a practical neural network text and will be an excellent resource for those who want to implement neural networks, rather than just learn the theory.' Scientific Computing World, September 1997