Supervised Learning With Complex-valued Neural Networks

Forecasting is one of the most important activities that form the basis for strategic, tactical, and operational decisions in all business organizations. Recently, neural networks have emerged as an i

This book provides a thorough theoretical and practical introduction to the application of neural networks to pattern recognition and intelligent signal processing. It has been tested on students, unf

Artificial Neural Networks (ANNs) offer an efficient method for finding optimal cleanup strategies for hazardous plumes contaminating groundwater by allowing hydrologists to rapidly search through mil

This timely overview and synthesis of recent work in both artificial neural networks and neurobiology seeks to examine neurobiological data from a network perspective and to encourage neuroscientists

A new, dramatically updated edition of the classic resource on the constantlyevolving fields of brain theory and neural networks.

This textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structure

Most practical applications of artificial neural networks are based on acomputational model involving the propagation of continuous variables from one processing unit tothe next. In recent years, data

This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques fo

This excellent reference for all those involved in neural networks research and application presents, in a single text, the necessary aspects of parallel implementation for all major artificial neural

Most practical applications of artificial neural networks are based on acomputational model involving the propagation of continuous variables from one processing unit tothe next. In recent years, data

Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificia

Neural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought to

Systematically explores the relationship between principal component analysis (PCA) and neural networks. Provides a synergistic examination of the mathematical, algorithmic, application and architectu

Artificial Neural Networks (ANNs) offer an efficient method for finding optimal cleanup strategies for hazardous plumes contaminating groundwater by allowing hydrologists to rapidly search through mil

Understand the fundamentals of the emerging field of fuzzy neural networks, their applications and the most used paradigms with this carefully organized state-of-the-art textbook. Previously tested at

Choice Outstanding Academic Title, 1996.An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more than a decade, it is aimed

This timely overview and synthesis of recent work in both artificial neural networks and neurobiology seeks to examine neurobiological data from a network perspective and to encourage neuroscientists

Neural Network Learning and Expert Systems is the first book to present a unified and in-depth development of neural network learning algorithms and neural network expert systems. Especially suitable

One of the most exciting and potentially rewarding areas of scientific research is the study of the principles and mechanisms underlying brain function. It is also of great promise to future generations of computers. A growing group of researchers, adapting knowledge and techniques from a wide range of scientific disciplines, have made substantial progress understanding memory, the learning process, and self organization by studying the properties of models of neural networks - idealized systems containing very large numbers of connected neurons, whose interactions give rise to the special qualities of the brain. This book introduces and explains the techniques brought from physics to the study of neural networks and the insights they have stimulated. It is written at a level accessible to the wide range of researchers working on these problems - statistical physicists, biologists, computer scientists, computer technologists and cognitive psychologists. The author presents a coherent a

This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely “from scratch” based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.

This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely “from scratch” based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.

AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. It comprises of bootstrapping to create multiple training and testing

This title covers key topics and approaches for next generation networks and complex communication systems, and the various aspects of modeling and simulation of these networks from a complex adaptive

This textbook provides a general introduction to the field of neural networks. Thoroughly revised and updated from the previous editions of 1991 and 2000, the current edition concentrates on networks

Spiking neural networks (SNN) are biologically inspired computational models that represent and process information internally as trains of spikes. This monograph book presents the classical theory an

This book introduces recent results on output synchronization of complex dynamical networks with single and multiple weights. It discusses novel research ideas and a number of definitions in complex d

The auditory system is a complex neural system composed of many types of neurons connected into networks. One feature that sets the auditory system apart from other sensory systems, such as somatosens

Advances in complex networks and graph signal processing have important implications in fields ranging from communications and social networking to big data and biology. In Complex Networks, three pio

This book constitutes the refereed proceedings of the 18th International Conference on Engineering Applications of Neural Networks, EANN 2017, held in Athens, Greece, in August 2017. The 40 revised fu

Businesses and consumers depend on reliable computer networks for banking, learning, entertainment, and staying in touch. Cisco is the leader in networking. Cisco skills are highly valued and there ar

The book is an authoritative collection of contributions by leading experts on the topics of fuzzy logic, multi-valued logic and neural network. Originally written as an homage to Claudio Moraga, seen

This book highlights the ability of neural networks (NNs) to be excellent pattern matchers and their importance in information retrieval (IR), which is based on index term matching. The book defines a

This book covers the fundamental topics that form the basis of computational intelligence, which are neural networks, fuzzy systems, and evolutionary computation. Unlike other introductory texts on co

In recent years, Higher Order Neural Networks (HONNs) have been widely adopted by researchers for applications in control signal generating, pattern recognition, nonlinear recognition, classification,

The volume LNCS 9377 constitutes the refereed proceedings of the 12th International Symposium on Neural Networks, ISNN 2015, held in jeju, South Korea on October 2015. The 55 revised full papers prese

This book presents novel methodologies for modeling, simulation and control of gas turbines using ANN (Artificial Neural Networks). Readers will learn how artificial intelligent techniques can be used

This book constitutes the refereed proceedings of the 16th International Conference on Engineering Applications of Neural Networks, EANN 2015, held in Rhodes, Greece, in September 2015.The 36 revised

This book reports on the latest findings in the study of Stochastic Neural Networks (SNN). The book collects the novel model of the disturbance driven by Levy process, the research method of M-matrix

Centered around major topic areas of both theoretical and practical importance, the World Congress on Neural Networks provides its registrants -- from a diverse background encompassing industry, acade

"This book provides a comprehensive review of new developments in the study of language processing and related neural networks in schizophrenia by addressing the complex link between psychopathology,