Intelligent systems and related designs have become important instruments leading to profound innovations in automated control and interaction with computers and machines. Such systems depend upon established methods and tools for solving complex learning and decision-making problems under uncertain and continuously varying conditions. Intelligent Applications for Heterogeneous System Modeling and Design examines the latest developments in intelligent system engineering being used across i...

It is a compilation of research works related to intelligent and emerging system design using a range of tools including soft-computation. The book includes reviews, actual designs, research works, discussion and experimental results related to works in the areas of communication, computation, vision sciences, bio-inspired system design, social dynamic, related process design, etc. The audience of this book is expected to be researchers who deal with intelligent and emerging system design ...

The book discusses intelligent system design using soft computing and similar systems and their interdisciplinary applications. It also focuses on the recent trends to use soft computing as a versatile tool for designing a host of decision support systems.

In From Molecule to Metaphor, Jerome Feldman proposes a theory of language and thought that treats language not as an abstract symbol system but as a human biological ability that can be studied as a function of the brain, as vision and motor control are studied. This theory, he writes, is a "bridging theory" that works from extensive knowledge at two ends of a causal chain to explicate the links between. Although the cognitive sciences are revealing much about how our brains prod...

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics ...

This concise and accessible textbook supports a foundation or module course on A.I., covering a broad selection of the subdisciplines within this field. The book presents concrete algorithms and applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks and reinforcement learning. Topics and features: presents an application-focused and hands-on approach to learning the subject; provides study exercises of varying degrees of difficult...

This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in m...

This book provides a historic overview of what is defined as "soft computing" with its techniques: neural networks, fuzzy logic, genetic algorithms and cellular automata.

Graphical models in their modern form have been around since the late 1970s and appear today in many areas of the sciences. Along with the ongoing developments of graphical models, a number of different graphical modeling software programs have been written over the years. In recent years many of these software developments have taken place within the R community, either in the form of new packages or by providing an R interface to existing software. This book attempts to give the reader a...

This book provides a broad survey of advanced pattern recognition techniques for human behavior analysis. Clearly structured, the book begins with concise coverage of the major concepts, before introducing the most frequently used techniques and algorithms in detail, and then discussing examples of real applications. Features: contains contributions from an international selection of experts in the field; presents a thorough introduction to the fundamental topics of human behavior analysis...

The concept generation process seems like an intuitional thought: difficult to capture and perform, although everyone is capable of it. It is not an analytical process but a synthetic process which has yet to be clarified. Furthermore, new research methods for investigating the concept generation process—a very difficult task since the concept generation process is driven by inner feelings deeply etched in the mind—are necessary to establish its theory and methodology.Concept Gener...

Markov ModelsThis book will offer you an insight into the Hidden Markov Models as well as the Bayesian Networks. Additionally, by reading this book, you will also learn algorithms such as Markov Chain Sampling.Furthermore, this book will also teach you how Markov Models are very relevant when a decision problem is associated with a risk that continues over time, when the timing of occurrenc...