For convenience, many of the proofs of the key theorems have been rewritten so that the entire book uses a relatively uniform notion.

"Neural Networks have influenced many areas of research but have only just started to be utilized in social science research. Neural Networks provides the first accessible introduction to this analysis as a powerful method for social scientists. It provides numerous studies and examples that illustrate the advantages of neural network analysis over other quantitative and modeling methods in wide spread use among social scientists. The author presents the methods in an accessible style for the reader who does not have a background in computer science. Features include an introduction to the vocabulary and framework of neural networks, a concise history of neural network methods, a substantial review of the literature, detailed neural network applications in the social sciences, coverage of the most common alternative neural network models, methodological considerations in applying neural networks, examples using the two leading software packages for neural network analysis, and numerous illustrations and diagrams."--Pub. desc.

The aim of this book is to handle different application problems of science and engineering using expert Artificial Neural Network (ANN). As such, the book starts with basics of ANN along with different mathematical preliminaries with respect to algebraic equations. Then it addresses ANN based methods for solving different algebraic equations viz. polynomial equations, diophantine equations, transcendental equations, system of linear and nonlinear equations, eigenvalue problems etc. which are the basic equations to handle the application problems mentioned in the content of the book. Although there exist various methods to handle these problems, but sometimes those may be problem dependent and may fail to give a converge solution with particular discretization. Accordingly, ANN based methods have been addressed here to solve these problems. Detail ANN architecture with step by step procedure and algorithm have been included. Different example problems are solved with respect to various application and mathematical problems. Convergence plots and/or convergence tables of the solutions are depicted to show the efficacy of these methods. It is worth mentioning that various application problems viz. Bakery problem, Power electronics applications, Pole placement, Electrical Network Analysis, Structural engineering problem etc. have been solved using the ANN based methods.

The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised, unsupervised, and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive, batch, minibatch, MCEM, and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics, computer science, electrical engineering, and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students, professional engineers, and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph.D., M.S.E.E., B.S.E.E.) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models.

This highly interdisciplinary book discusses the phenomenon of life, including its origin and evolution, against the background of thermodynamics, statistical mechanics, and information theory. Among the central themes is the seeming contradiction between the second law of thermodynamics and the high degree of order and complexity produced by living systems. As the author shows, this paradox has its resolution in the information content of the Gibbs free energy that enters the biosphere from outside sources. Another focus of the book is the role of information in human cultural evolution, which is also discussed with the origin of human linguistic abilities. One of the final chapters addresses the merging of information technology and biotechnology into a new discipline — bioinformation technology.This third edition has been updated to reflect the latest scientific and technological advances. Professor Avery makes use of the perspectives of famous scholars such as Professor Noam Chomsky and Nobel Laureates John O'Keefe, May-Britt Moser and Edward Moser to cast light on the evolution of human languages. The mechanism of cell differentiation, and the rapid acceleration of information technology in the 21st century are also discussed.With various research disciplines becoming increasingly interrelated today, Information Theory and Evolution provides nuance to the conversation between bioinformatics, information technology, and pertinent social-political issues. This book is a welcome voice in working on the future challenges that humanity will face as a result of scientific and technological progress.

This study explores the design and application of natural language text-based processing systems, based on generative linguistics, empirical copus analysis, and artificial neural networks. It emphasizes the practical tools to accommodate the selected system.

Information Theory and Evolution discusses the phenomenon of life, including its origin and evolution (and also human cultural evolution), against the background of thermodynamics, statistical mechanics, and information theory. Among the central themes is the seeming contradiction between the second law of thermodynamics and the high degree of order and complexity produced by living systems. This paradox has its resolution in the information content of the Gibbs free energy that enters the biosphere from outside sources, as the author will show. The role of information in human cultural evolution is another focus of the book.The first edition of Information Theory and Evolution made a strong impact on thought in the field by bringing together results from many disciplines. The new second edition offers updated results based on reports of important new research in several areas, including exciting new studies of the human mitochondrial and Y-chromosomal DNA. Another extensive discussion featured in the second edition is contained in a new appendix devoted to the relationship of entropy and Gibbs free energy to economics. This appendix includes a review of the ideas of Alfred Lotka, Frederick Soddy, Nicholas Georgiescu-Roegen and Herman E. Daly, and discusses the relevance of these ideas to the current economic crisis.The new edition discusses current research on the origin of life, the distinction between thermodynamic information and cybernetic information, new DNA research and human prehistory, developments in current information technology, and the relationship between entropy and economics.