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Advanced Algorithms for Neural Networks: A C++ Sourcebook

Overview

This practical guide contains a wide variety of state-of-the-art algorithms that are useful in the design and implementation of neural networks. All algorithms are presented on both an intuitive and a theoretical level, with complete source code provided on an accompanying disk. Several training algorithms for multiple-layer feedforward networks (MLFN) are featured. The probabilistic neural network is extended to allow separate sigmas for each variable, and even separate sigma vectors for each class. The ...
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Overview

This practical guide contains a wide variety of state-of-the-art algorithms that are useful in the design and implementation of neural networks. All algorithms are presented on both an intuitive and a theoretical level, with complete source code provided on an accompanying disk. Several training algorithms for multiple-layer feedforward networks (MLFN) are featured. The probabilistic neural network is extended to allow separate sigmas for each variable, and even separate sigma vectors for each class. The generalized regression neural network is similarly extended, and a fast second-order training algorithm for all of these models is provided. The book also discusses the recently developed Gram-Charlier neural network and provides important information on its strengths and weaknesses. Readers are shown several proven methods for reducing the dimensionality of the input data. Advanced Algorithms for Neural Networks also covers advanced multiple-sigma PNN and GRNN training, including conjugate-gradient optimization based on cross validation, the Levenberg-Marquardt training algorithm for multiple-layer feedforward networks, advanced stochastic optimization, including Cauchy simulated annealing and stochastic smoothing, data reduction and orthogonalization via principal components and discriminant functions, economical yet powerful validation techniques, including the jack-knife, the bootstrap, and cross validation and includes a complete state-of-the-art PNN/GRNN program, with both source and executable code.

This is one of the first books to offer practical in-depth coverage of the Probabilistic Neural Network (PNN) and several other neural nets and their related algorithms critical to solving some of today's toughest real-world computing problems. Includes complete C++ source code for basic and advanced applications.

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Editorial Reviews

Booknews
This book/disk combination presents programmers with algorithms useful in the design and implementation of neural networks, explaining algorithms on both an intuitive and a theoretical level. The book discusses the probabalistic neural network and the generalized regression neural network, provides a second-order training algorithm for these models, and reports on the strengths and weaknesses of the newly developed Gram-Charlier neural network. The disk contains complete source code for algorithms. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780471105886
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 3/17/1995
  • Edition description: BOOK&DISK
  • Edition number: 1
  • Pages: 448
  • Product dimensions: 7.47 (w) x 9.25 (h) x 1.10 (d)

Table of Contents

1 Deterministic Optimization 1
2 Stochastic Optimization 73
3 Hybrid Training Algorithms 135
4 Probabilistic Neural Networks I: Introduction 157
5 Probabilistic Neural Networks II: Advanced Techniques 193
6 Generalized Regression 223
7 The Gram-Charlier Neural Network 251
8 Dimension Reduction and Orthogonalization 293
9 Assessing Generalization Ability 335
10 Using the PNN Program 379
Appendix 403
Bibliography 407
Index 427
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