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Discrete mathematics of neu...
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 functions and the simplest types of neural networks. 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, computational 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
Monografía
monografia Rebiun37144458 https://catalogo.rebiun.org/rebiun/record/Rebiun37144458 m eo d cr bn |||m|||a 101020s2001 pau ob 001 0 eng d 00067940 0-89871-853-8 DT08 siam DT08 SIAM CUNEF 991000509032208131 CaBNVSL. CaBNVSL. CaBNVSL eng 006.3/2/0151 21 Anthony, Martin Discrete mathematics of neural networks selected topics Martin Anthony Philadelphia, Pa. Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104) 2001 Philadelphia, Pa. Philadelphia, Pa. Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104) 1 electronic text (xi, 131 p.) digital file 1 electronic text (xi, 131 p.) Text txt computer c online resource cr SIAM monographs on discrete mathematics and applications Bibliographic Level Mode of Issuance: Monograph Includes bibliographical references (p. 119-125) and index Artificial Neural Networks -- Boolean Functions -- Threshold Functions -- Number of Threshold Functions -- Sizes of Weights for Threshold Functions -- Threshold Order -- Threshold Networks and Boolean Functions -- Specifying Sets -- Neural Network Learning -- Probabilistic Learning -- VC-Dimensions of Neural Networks -- The Complexity of Learning -- Boltzmann Machines and Combinatorial Optimization 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 functions and the simplest types of neural networks. 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, computational 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 Also available in print version Mode of access: World Wide Web System requirements: Adobe Acrobat Reader English Neural networks (Computer science)- Mathematics Society for Industrial and Applied Mathematics 0-89871-480-X SIAM monographs on discrete mathematics and applications