Download or read book Classes of Feedforward Neural Networks and Their Circuit Comlexity written by John Shawe-Taylor and published by . This book was released on 1990 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Handbook of Neural Computation written by E Fiesler and published by CRC Press. This book was released on 2020-01-15 with total page 1094 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Neural Computation is a practical, hands-on guide to the design and implementation of neural networks used by scientists and engineers to tackle difficult and/or time-consuming problems. The handbook bridges an information pathway between scientists and engineers in different disciplines who apply neural networks to similar probl
Download or read book Theory and Applications of Models of Computation written by Mitsunori Ogihara and published by Springer. This book was released on 2011-04-27 with total page 577 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th International Conference on Theory and Applications of Models of Computation, TAMC 2011, held in Tokyo, Japan, in May 2011. The 51 revised full papers presented together with the abstracts of 2 invited talks were carefully reviewed and selected from 136 submissions. The papers address the three main themes of the conference which were computability, complexity, and algorithms and are organized in topical sections on general algorithms, approximation, graph algorithms, complexity, optimization, circuit complexity, data structures, logic and formal language theory, games and learning theory, and cryptography and communication complexity.
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1991 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book World Congress on Neural Networks written by Paul Werbos and published by Routledge. This book was released on 2021-09-09 with total page 860 pages. Available in PDF, EPUB and Kindle. Book excerpt: Centered around 20 major topic areas of both theoretical and practical importance, the World Congress on Neural Networks provides its registrants -- from a diverse background encompassing industry, academia, and government -- with the latest research and applications in the neural network field.
Download or read book Cybernetics And Systems 94 Proceedings Of The 12th European Meeting On Cybernetics And Systems Research In 2 Volumes written by Robert Trappl and published by World Scientific. This book was released on 1994-03-15 with total page 1964 pages. Available in PDF, EPUB and Kindle. Book excerpt: The papers in this volume reflect the most recent research findings in cybernetics and systems research. They were selected from 298 draft final papers which were submitted to the conference by authors from more than 30 different countries from five continents.
Download or read book Limitations and Future Trends in Neural Computation written by Sergey Ablameyko and published by IOS Press. This book was released on 2003 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work reports critical analyses on complexity issues in the continuum setting and on generalization to new examples, which are two basic milestones in learning from examples in connectionist models. It also covers up-to-date developments in computational mathematics.
Download or read book Artificial Neural Nets and Genetic Algorithms written by David W. Pearson and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural networks and genetic algorithms both are areas of research which have their origins in mathematical models constructed in order to gain understanding of important natural processes. By focussing on the process models rather than the processes themselves, significant new computational techniques have evolved which have found application in a large number of diverse fields. This diversity is reflected in the topics which are subjects of the contributions to this volume. There are contributions reporting successful applications of the technology to the solution of industrial/commercial problems. This may well reflect the maturity of the technology, notably in the sense that 'real' users of modelling/prediction techniques are prepared to accept neural networks as a valid paradigm. Theoretical issues also receive attention, notably in connection with the radial basis function neural network. Contributions in the field of genetic algorithms reflect the wide range of current applications, including, for example, portfolio selection, filter design, frequency assignment, tuning of nonlinear PID controllers. These techniques are also used extensively for combinatorial optimisation problems.
Download or read book Advances in Neural Information Processing Systems 8 written by David S. Touretzky and published by MIT Press. This book was released on 1996 with total page 1128 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decade has seen greatly increased interaction between theoretical work in neuroscience, cognitive science and information processing, and experimental work requiring sophisticated computational modeling. The 152 contributions in NIPS 8 focus on a wide variety of algorithms and architectures for both supervised and unsupervised learning. They are divided into nine parts: Cognitive Science, Neuroscience, Theory, Algorithms and Architectures, Implementations, Speech and Signal Processing, Vision, Applications, and Control. Chapters describe how neuroscientists and cognitive scientists use computational models of neural systems to test hypotheses and generate predictions to guide their work. This work includes models of how networks in the owl brainstem could be trained for complex localization function, how cellular activity may underlie rat navigation, how cholinergic modulation may regulate cortical reorganization, and how damage to parietal cortex may result in neglect. Additional work concerns development of theoretical techniques important for understanding the dynamics of neural systems, including formation of cortical maps, analysis of recurrent networks, and analysis of self- supervised learning. Chapters also describe how engineers and computer scientists have approached problems of pattern recognition or speech recognition using computational architectures inspired by the interaction of populations of neurons within the brain. Examples are new neural network models that have been applied to classical problems, including handwritten character recognition and object recognition, and exciting new work that focuses on building electronic hardware modeled after neural systems. A Bradford Book
Download or read book Learning with Recurrent Neural Networks written by Barbara Hammer and published by Springer. This book was released on 2007-10-03 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: Folding networks, a generalisation of recurrent neural networks to tree structured inputs, are investigated as a mechanism to learn regularities on classical symbolic data, for example. The architecture, the training mechanism, and several applications in different areas are explained. Afterwards a theoretical foundation, proving that the approach is appropriate as a learning mechanism in principle, is presented: Their universal approximation ability is investigated- including several new results for standard recurrent neural networks such as explicit bounds on the required number of neurons and the super Turing capability of sigmoidal recurrent networks. The information theoretical learnability is examined - including several contribution to distribution dependent learnability, an answer to an open question posed by Vidyasagar, and a generalisation of the recent luckiness framework to function classes. Finally, the complexity of training is considered - including new results on the loading problem for standard feedforward networks with an arbitrary multilayered architecture, a correlated number of neurons and training set size, a varying number of hidden neurons but fixed input dimension, or the sigmoidal activation function, respectively.
Download or read book Integer Programming and Combinatorial Optimization written by Alberto Del Pia and published by Springer Nature. This book was released on 2023-05-21 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 24th International Conference on Integer Programming and Combinatorial Optimization, IPCO 2023, held in Madison, WI, USA, during June 21–23, 2023. The 33 full papers presented were carefully reviewed and selected from 119 submissions. IPCO is under the auspices of the Mathematical Optimization Society, and it is an important forum for presenting present recent developments in theory, computation, and applications. The scope of IPCO is viewed in a broad sense, to include algorithmic and structural results in integer programming and combinatorial optimization as well as revealing computational studies and novel applications of discrete optimization to practical problems.
Download or read book Complex Valued Neural Networks Utilizing High Dimensional Parameters written by Nitta, Tohru and published by IGI Global. This book was released on 2009-02-28 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book covers the current state-of-the-art theories and applications of neural networks with high-dimensional parameters"--Provided by publisher.
Download or read book Handbook of Approximation Algorithms and Metaheuristics written by Teofilo F. Gonzalez and published by CRC Press. This book was released on 2018-05-15 with total page 840 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Approximation Algorithms and Metaheuristics, Second Edition reflects the tremendous growth in the field, over the past two decades. Through contributions from leading experts, this handbook provides a comprehensive introduction to the underlying theory and methodologies, as well as the various applications of approximation algorithms and metaheuristics. Volume 1 of this two-volume set deals primarily with methodologies and traditional applications. It includes restriction, relaxation, local ratio, approximation schemes, randomization, tabu search, evolutionary computation, local search, neural networks, and other metaheuristics. It also explores multi-objective optimization, reoptimization, sensitivity analysis, and stability. Traditional applications covered include: bin packing, multi-dimensional packing, Steiner trees, traveling salesperson, scheduling, and related problems. Volume 2 focuses on the contemporary and emerging applications of methodologies to problems in combinatorial optimization, computational geometry and graphs problems, as well as in large-scale and emerging application areas. It includes approximation algorithms and heuristics for clustering, networks (sensor and wireless), communication, bioinformatics search, streams, virtual communities, and more. About the Editor Teofilo F. Gonzalez is a professor emeritus of computer science at the University of California, Santa Barbara. He completed his Ph.D. in 1975 from the University of Minnesota. He taught at the University of Oklahoma, the Pennsylvania State University, and the University of Texas at Dallas, before joining the UCSB computer science faculty in 1984. He spent sabbatical leaves at the Monterrey Institute of Technology and Higher Education and Utrecht University. He is known for his highly cited pioneering research in the hardness of approximation; for his sublinear and best possible approximation algorithm for k-tMM clustering; for introducing the open-shop scheduling problem as well as algorithms for its solution that have found applications in numerous research areas; as well as for his research on problems in the areas of job scheduling, graph algorithms, computational geometry, message communication, wire routing, etc.
Download or read book Feedforward Neural Network Methodology written by Terrence L. Fine and published by Springer Science & Business Media. This book was released on 2006-04-06 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: This decade has seen an explosive growth in computational speed and memory and a rapid enrichment in our understanding of artificial neural networks. These two factors provide systems engineers and statisticians with the ability to build models of physical, economic, and information-based time series and signals. This book provides a thorough and coherent introduction to the mathematical properties of feedforward neural networks and to the intensive methodology which has enabled their highly successful application to complex problems.
Download or read book Dynamic Complexity Reduction of Multilayer Feedforward Neural Networks written by Damon A. Miller and published by . This book was released on 1997 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Neural Networks and Statistical Learning written by Ke-Lin Du and published by Springer Nature. This book was released on 2019-09-12 with total page 988 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.
Download or read book Boolean Models and Methods in Mathematics Computer Science and Engineering written by Yves Crama and published by Cambridge University Press. This book was released on 2010-06-28 with total page 781 pages. Available in PDF, EPUB and Kindle. Book excerpt: A collection of papers written by prominent experts that examine a variety of advanced topics related to Boolean functions and expressions.
