Using real-life examples to illustrate the performance of learning
algorithms and instructing readers how to apply them to practical
applications, this work offers a comprehensive treatment of
subspace learning algorithms for neural networks. The authors
summarize a decade of high quality research offering a host of
practical applications. They demonstrate ways to extend the use of
algorithms to fields such as encryption communication, data mining,
computer vision, and signal and image processing to name just a
few. The brilliance of the work lies with how it coherently builds
a theoretical understanding of the convergence behavior of subspace
learning algorithms through a summary of chaotic behaviors.
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