Автор: Vinit Kumar Gunjan, Jacek M. Zurada
Издательство: Springer
Год: 2020
Страниц: 243
Язык: английский
Формат: pdf (true)
Размер: 10.6 MB
This book discusses various Machine Learning & cognitive science approaches, presenting high-throughput research by experts in this area. Bringing together Machine Learning, cognitive science and other aspects of Artificial Intelligence (AI) to help provide a roadmap for future research on intelligent systems, the book is a valuable reference resource for students, researchers and industry practitioners wanting to keep abreast of recent developments in this dynamic, exciting and profitable research field.
This book makes few assertions about the reader’s context, due to the interdisciplinary nature of the content. Additionally, it incorporates fundamental concepts from statistics, artificial intelligence, information theory and other fields as the need arises, concentrating on just those main concepts that are most applicable to machine learning and cognitive sciences. Through the discussion of select numbers of case studies, this book gives the researchers a detailed perspective of the vast panorama of research directions. This is in hope to help the readers with effective overview of applied machine learning, cognitive and related technologies.
This volume consists of 18 chapters, arranged on the basis of their approaches and contributions to the scope of this book. The chapters of this book present key algorithms and theories that form the core of the technologies and applications concerned, consisting mainly of face recognition, evolutionary algorithms such as genetic algorithms, automotive applications, automation devices with artificial neural networks, business management systems and modern speech processing systems. This book also covers recent advances in medical diagnostic systems, sensor networks and systems of VLSI domain.
It is intended for postgraduate students, researchers, scholars and developers who are interested in Machine Learning and cognitive research, and is also suitable for senior undergraduate courses in related topics. Further, it is useful for practitioners dealing with advanced data processing, applied mathematicians, developers of software for agent-oriented systems and developers of embedded and real-time systems.
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