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Advanced Algorithms for Neural Networks: A C++ Sourcebook,Used
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A valuable working resource for anyone who uses neural networks to solve realworld problemsThis practical guide contains a wide variety of stateoftheart 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 multiplelayer 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 secondorder training algorithm for all of these models is provided. The book also discusses the recently developed GramCharlier 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 multiplesigma PNN and GRNN training, including conjugategradient optimization based on cross validation The LevenbergMarquardt training algorithm for multiplelayer 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 stateoftheart PNN/GRNN program, with both source and executable code
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