Research Catalog

Title

Advanced algorithms for neural networks : a C++ sourcebook / Timothy Masters.

Author

Masters, Timothy

Publication

New York : Wiley, ©1995.

Items in the Library & Off-site

Details

Description

xiv, 431 pages : illustrations; 24 cm +

Summary

• 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.

Alternative Title

C plus plus sourcebook

Subject

• Neural networks (Computer science)
• Computer algorithms
• C++ (Computer program language)
• Algorithms
• Neural Networks, Computer
• Algorithms
• Réseaux neuronaux (Informatique)
• Algorithmes
• C++ (Langage de programmation)
• algorithms
• Computer algorithms
• Artificial intelligence

Bibliography (note)

• Includes bibliographical references (p. [407]-425) and index.

System Details (note)

• System requirements for accompanying computer disk: C++.

Contents

1. Deterministic Optimization -- 2. Stochastic Optimization -- 3. Hybrid Training Algorithms -- 4. Probabilistic Neural Networks I: Introduction -- 5. Probabilistic Neural Networks II: Advanced Techniques -- 6. Generalized Regression -- 7. The Gram-Charlier Neural Network -- 8. Dimension Reduction and Orthogonalization -- 9. Assessing Generalization Ability -- 10. Using the PNN Program.

ISBN

• 0471105880
• 9780471105886

LCCN

• 94043390
• 9780471105886

OCLC

• ocm31608883
• 31608883
• SCSB-9192363

Owning Institutions

Princeton University Library