Advanced Algorithms for Neural Networks: A C++ Sourcebook

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9780471105886: Advanced Algorithms for Neural Networks: A C++ Sourcebook

A valuable working resource for anyone who uses neural networks to solve real-world problems

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 jackknife, the bootstrap, and cross validation
  • Includes a complete state-of-the-art PNN/GRNN program, with both source and executable code

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From the Publisher:

A detailed study of the probabilistic neural network (PNN) and others as well as the algorithms that are important for solving several of the most difficult and critical computing problems. Dicusses major design issues and presents many automated techniques for adjusting the training set to increase its speed. Features in-depth coverage of cross-validation to improve performance. The accompanying disk contains a complete working program for training and testing cutting-edge PNNs plus full C++ source code and example applications.

About the Author:

TIMOTHY MASTERS received his PhD in mathematics in 1981. Since then he has worked as a consultant to the defense community and to industry. He is the author of Practical Neural Network Recipes in C++ and Signal and Image Processing with Neural Networks: A C++ Sourcebook. His current research focuses on high-level image understanding using artificial intelligence and neural networks.

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Masters, Timothy
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