Multidisciplinary Approaches to Neural Computing - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Multidisciplinary Approaches to Neural Computing write by Anna Esposito. This book was released on 2017-08-28. Multidisciplinary Approaches to Neural Computing available in PDF, EPUB and Kindle. This book presents a collection of contributions in the field of Artificial Neural Networks (ANNs). The themes addressed are multidisciplinary in nature, and closely connected in their ultimate aim to identify features from dynamic realistic signal exchanges and invariant machine representations that can be exploited to improve the quality of life of their end users. Mathematical tools like ANNs are currently exploited in many scientific domains because of their solid theoretical background and effectiveness in providing solutions to many demanding tasks such as appropriately processing (both for extracting features and recognizing) mono- and bi-dimensional dynamic signals, solving strong nonlinearities in the data and providing general solutions for deep and fully connected architectures. Given the multidisciplinary nature of their use and the interdisciplinary characterization of the problems they are applied to – which range from medicine to psychology, industrial and social robotics, computer vision, and signal processing (among many others) – ANNs may provide a basis for redefining the concept of information processing. These reflections are supported by theoretical models and applications presented in the chapters of this book. This book is of primary importance for: (a) the academic research community, (b) the ICT market, (c) PhD students and early-stage researchers, (d) schools, hospitals, rehabilitation and assisted-living centers, and (e) representatives of multimedia industries and standardization bodies.
Combining Artificial Neural Nets
Combining Artificial Neural Nets - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Combining Artificial Neural Nets write by Amanda J.C. Sharkey. This book was released on 2012-12-06. Combining Artificial Neural Nets available in PDF, EPUB and Kindle. 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.
Handbook of Neural Computation
Handbook of Neural Computation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Handbook of Neural Computation write by Pijush Samui. This book was released on 2017-07-18. Handbook of Neural Computation available in PDF, EPUB and Kindle. 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
Neural Computing - An Introduction
Neural Computing - An Introduction - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Neural Computing - An Introduction write by R Beale. This book was released on 1990-01-01. Neural Computing - An Introduction available in PDF, EPUB and Kindle. Neural computing is one of the most interesting and rapidly growing areas of research, attracting researchers from a wide variety of scientific disciplines. Starting from the basics, Neural Computing covers all the major approaches, putting each in perspective in terms of their capabilities, advantages, and disadvantages. The book also highlights the applications of each approach and explores the relationships among models developed and between the brain and its function. A comprehensive and comprehensible introduction to the subject, this book is ideal for undergraduates in computer science, physicists, communications engineers, workers involved in artificial intelligence, biologists, psychologists, and physiologists.
Second-Order Methods for Neural Networks
Second-Order Methods for Neural Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Second-Order Methods for Neural Networks write by Adrian J. Shepherd. This book was released on 1997-04-28. Second-Order Methods for Neural Networks available in PDF, EPUB and Kindle. This volume aims to develop the reader's understanding of the theoretical and practical issues involved in the development of efficient MLP training strategies, and to describe and evaluate the performance of a wide range of specific training algorithm. Particular emphasis is given to the development of methods which a strong theoretical foundation, rather than heuristic, "rule of thumb" training strategies. Second-Order Methods for Neural Networks will be of interest to academic researchers and postgraduate students working with neural networks (especially supervised learning with multi-layer perceptrons), industrial researchers and programmers developing neural network software, and professionals using neural networks as optimisation tools.
