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From Natural to Artificial Neural Computation

From Natural to Artificial Neural Computation
Author: Jose Mira
Publisher: Springer Science & Business Media
Total Pages: 1182
Release: 1995-05-24
Genre: Computers
ISBN: 9783540594970
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This volume presents the proceedings of the International Workshop on Artificial Neural Networks, IWANN '95, held in Torremolinos near Malaga, Spain in June 1995. The book contains 143 revised papers selected from a wealth of submissions and five invited contributions; it covers all current aspects of neural computation and presents the state of the art of ANN research and applications. The papers are organized in sections on neuroscience, computational models of neurons and neural nets, organization principles, learning, cognitive science and AI, neurosimulators, implementation, neural networks for perception, and neural networks for communication and control.

Handbook of Neural Computation

Handbook of Neural Computation
Author: Pijush Samui
Publisher: Academic Press
Total Pages: 658
Release: 2017-07-18
Genre: Technology & Engineering
ISBN: 0128113197
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Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world. Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text. Features high-quality research articles on multivariate adaptive regression splines, the minimax probability machine, and more Discusses machine learning techniques, including classification, clustering, regression, web mining, information retrieval and natural language processing Covers supervised, unsupervised, reinforced, ensemble, and nature-inspired learning methods

Unsupervised Learning

Unsupervised Learning
Author: Geoffrey Hinton
Publisher: MIT Press
Total Pages: 420
Release: 1999-05-24
Genre: Medical
ISBN: 9780262581684
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Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years. This volume of Foundations of Neural Computation, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.

Artificial Neural Networks

Artificial Neural Networks
Author: Fouad Sabry
Publisher: One Billion Knowledgeable
Total Pages: 186
Release: 2023-06-21
Genre: Computers
ISBN:
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What Is Artificial Neural Networks Computing systems that are inspired by the biological neural networks that make up animal brains are called artificial neural networks (ANNs). These systems are more commonly referred to as neural networks (NNs) or neural nets. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Artificial neural network Chapter 2: Artificial neuron Chapter 3: Unsupervised learning Chapter 4: Backpropagation Chapter 5: Types of artificial neural networks Chapter 6: Deep learning Chapter 7: Convolutional neural network Chapter 8: Long short-term memory Chapter 9: Recurrent neural network Chapter 10: History of artificial neural networks (II) Answering the public top questions about artificial neural networks. (III) Real world examples for the usage of artificial neural networks in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of artificial neural networks. What Is Artificial Intelligence Series The Artificial Intelligence eBook series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The Artificial Intelligence eBook series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

Artificial Neural Network Modelling

Artificial Neural Network Modelling
Author: Subana Shanmuganathan
Publisher: Springer
Total Pages: 468
Release: 2016-02-03
Genre: Technology & Engineering
ISBN: 3319284959
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This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling.

Combining Artificial Neural Nets

Combining Artificial Neural Nets
Author: Amanda J.C. Sharkey
Publisher: Springer Science & Business Media
Total Pages: 300
Release: 2012-12-06
Genre: Computers
ISBN: 1447107934
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This volume, written by leading researchers, presents methods of combining neural nets to improve their performance. The techniques include ensemble-based approaches, where a variety of methods are used to create a set of different nets trained on the same task, and modular approaches, where a task is decomposed into simpler problems. The techniques are also accompanied by an evaluation of their relative effectiveness and their application to a variety of problems.

Trends in Neural Computation

Trends in Neural Computation
Author: Ke Chen
Publisher: Springer
Total Pages: 510
Release: 2006-11-15
Genre: Computers
ISBN: 3540361227
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Trends in Neural Computation includes twenty chapters contributed by leading experts or formed by extending well-selected papers presented in the 2005 International Conference on Natural Computation. The book reviews the latest progress in a range of different areas of neural computation, including theoretical neural computation, biologically plausible neural modeling, computational cognitive science, artificial neural networks – architectures and learning algorithms and their applications in real-world problems.