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Neural Networks and Statistical Learning (Paperback, 2nd ed. 2019)
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Neural Networks and Statistical Learning (Paperback, 2nd ed. 2019)
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This book provides a broad yet detailed introduction to neural
networks and machine learning in a statistical framework. A single,
comprehensive resource for study and further research, it explores
the major popular neural network models and statistical learning
approaches with examples and exercises and allows readers to gain a
practical working understanding of the content. This updated new
edition presents recently published results and includes six new
chapters that correspond to the recent advances in computational
learning theory, sparse coding, deep learning, big data and cloud
computing. 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.
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