`Discrete Mathematics of Neural Networks: Selected Topics'
''This concise, readable book provides a sampling of the very large, active, and expanding field of artificial neural network theory. It considers select areas of discrete mathematics linking combinatorics and the theory of the simplest types of artificial neural networks. Neural networks have emerged as a key technology in many fields of application, and an understanding of the theories concerning what such systems can and cannot do is essential. The author discusses interesting connections between special types of Boolean function and the simplest types of neural network. Some classical results are presented with accessible proofs, together with some more recent perspectives, such as those obtained by considering decision lists. In addition, probabilistic models of neural network learning are discussed. Graph theory, some partially ordered set theory, complexity, and discrete probability are among the mathematical topics involved. Pointers to further reading and an extensive bibliography make this book a good starting point for research in discrete mathematics and neural networks.
This book is aimed primarily at graduate
students and researchers in discrete mathematics. Readers need only a
basic knowledge of discrete mathematics and probability theory to enjoy
this book; no prior knowledge of neural networks is necessary. ''
well-written and rigorous monograph is intended for
mathematicians who wish to learn more about artificial neural networks.
....Researchers working on artificial neural network theory are likely
to find useful information in this volume." Journal of
"The book reveals interesting relationships between discrete mathematics and artificial Neural Networks" Zentralblatt MATH.