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Neural Networks and Statistical Learning
Neural Networks and Statistical Learning
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Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include:
• multilayer perceptron;
• the Hopfield network;
• associative memory models;• clustering models and algorithms;
• t he radial basis function network;
• recurrent neural networks;
• nonnegative matrix factorization;
• independent component analysis;
•probabilistic and Bayesian networks; and
• fuzzy sets and logic.
Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.