Providing a broad but in-depth introduction to neural network and
machine learning in a statistical framework, this book provides a
single, comprehensive resource for study and further research. All
the major popular neural network models and statistical learning
approaches are covered with examples and exercises in every chapter
to develop a practical working understanding of the content. Each
of the twenty-five chapters includes state-of-the-art descriptions
and important research results on the respective topics. The broad
coverage includes the multilayer perceptron, the Hopfield network,
associative memory models, clustering models and algorithms, the
radial basis function network, recurrent neural networks, principal
component analysis, nonnegative matrix factorization, independent
component analysis, discriminant analysis, support vector machines,
kernel methods, reinforcement learning, probabilistic and Bayesian
networks, data fusion and ensemble learning, fuzzy sets and logic,
neurofuzzy models, hardware implementations, and some machine
learning topics. Applications to biometric/bioinformatics and data
mining are also included. Focusing on the prominent accomplishments
and their practical aspects, academic and technical staff, graduate
students and researchers will find that this provides a solid
foundation and encompassing reference for the fields of neural
networks, pattern recognition, signal processing, machine learning,
computational intelligence, and data mining.
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