This book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Research on pattern classification with binary-output networks is surveyed, including a discussion of the relevance of the Vapnik-Chervonenkis dimension, and calculating estimates of the dimension for several neural network models. A model of classification by real-output networks is developed, and the usefulness of classification with a 'large margin' is demonstrated. The authors explain the role of scale-sensitive versions of the Vapnik-Chervonenkis dimension in large margin classification, and in real prediction. They also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient constructive learning algorithms. The book is self-contained and is intended to be accessible to researchers and graduate students in computer science, engineering, and mathematics.

Neural Network Learning: Theoretical Foundations

Computers & Technology

Anthony, Martin

Bartlett, Peter L.

Computer Science

Cambridge University Press

This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, Anthony and Bartlett develop a model of classification by real-output networks, and demonstrate the usefulness of classification with a "large margin." The authors explain the role of scale-sensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction. Key chapters also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient, constructive learning algorithms. The book is self-contained and accessible to researchers and graduate students in computer science, engineering, and mathematics.

Any student of linear algebra will welcome this textbook, which provides a thorough treatment of this key topic. Blending practice and theory, the book enables the reader to learn and comprehend the standard methods, with an emphasis on understanding how they actually work. At every stage, the authors are careful to ensure that the discussion is no more complicated or abstract than it needs to be, and focuses on the fundamental topics. The book is ideal as a course text or for self-study. Instructors can draw on the many examples and exercises to supplement their own assignments. End-of-chapter sections summarize the material to help students consolidate their learning as they progress through the book.

Linear Algebra: Concepts and Methods

Science & Math

Harvey, Michele

Mathematics

Without expecting any particular background of the reader, this book covers graphs and relations, sequences and limits, partial derivatives, optimization, vectors, and matrix algebra. Throughout, the stress is firmly on how the mathematics relates to economics, and this is illustrated with copious examples and exercises that will foster depth of understanding.

Mathematics for Economics and Finance: Methods and Modelling

Business & Money

Biggs, Norman

Economics

Certification

Theory and Practice of Astral Projection

Religion & Spirituality

Occult & Paranormal

Red Wheel/Weiser

The fact that there are special problems in the care of the elderly in the community is ample justification for writing a separate volume on the subject. The knowledge that there are increasing numbers of older people in virtually every country in the world makes it all the more important that the family physician should have sympathy with and understanding of the problems of them, since they are lilcely to make up a significant part of his worlcload and, increasingly, will take up more of his time and energy. There is a progressive amount of disability with advancing years, and this is particularly true of those aged 75 years and over. Increasing age is associated with serious impairment of hearing and vision, senses which younger people take for granted. Old age sees the arrival of major diseases of middle age with much greater frequency, such as ischaemic heart and cerebrovascular disease, diabetes mellitus and osteoarthritis. There are other disorders that are virtually confined to the elderly, such as fractures of the femoral neck, Paget's disease and myeloma. We now know that the disintegration of the con­ ducting tissue of the heart is largely an age-related phenomenon. Ageing processes affect every major organ system in the body and the impairment of physiological perfor­ mance resulting from these affects the elderly individual's response to infections, disease and environmental changes in complex ways that are not seen in younger people.

Problems in Geriatric Medicine (Problems in Practice)

Health, Fitness & Dieting

Springer

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. 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.

Discrete Mathematics of Neural Networks: Selected Topics (Monographs on Discrete Mathematics and Applications, Series Number 9)

Society for Industrial and Applied Mathematics

Mathematics for Economics and Finance

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Books by Anthony, Martin