Proceedings of the International Workshop on Applications of Neural Networks to Telecommunications


Book Description

The world is witnessing the rapid evolution of its own nervous system by an unparalleled growth in communication technology. Like the evolution of the nervous systems in animals, this growth is being driven by a survival-of-the-fittest-mechanism. In telecommunications, the entities that fuel this growth are companies and nations who compete with each other. Companies with superior information systems can outrun and outsmart others because they serve their customers better. On the threshold of an explosion in the variety, speed and usefulness of telecommunication networks, neural network researchers can make important contributions to this emerging new telecommunications infrastructure. The first International Workshop on Applications of Neural Networks to Telecommunications (IWANNT) was planned in response to the telecommunications industry's needs for new adaptive technologies. This workshop featured 50 talks and posters that were selected by an organizing committee of experts in both telecommunications and neural networks. These proceedings will also be available on-line in an electronic format providing multimedia figures, cross-referencing, and annotation.




IJCAI-89


Book Description




Machine Learning Proceedings 1989


Book Description

Machine Learning Proceedings 1989




Artificial Intelligence in Design '91


Book Description

Artificial Intelligence in Design '91 is a collection of 47 papers from the First International Conference on Artificial Intelligence in Design held at Edinburgh in June 1991. The papers in this book are grouped into 13 headings, starting with a background of AI design systems and to which extent AI that results from being used as planning tool be applied to quality-oriented design processes in architecture. A constraint-driven approach to object-oriented design is also shown on real-world objects. The use of CADSYN in the structural design of buildings is examined, along with design-dependent knowledge and design-independent knowledge. Discussions on empowering designers with integrated design environments are given whereby design objects may be retrieved from catalogues without requiring users to form queries. Mention is given to automated adjustment of parameter values frequently used in computer routine applications. The book also introduces the Computer Aided Design (CAD) as applied to architecture. Design representation using data models, non-monotonic reasoning in design, and the cognitive aspects of design using empirical studies are discussed. Topics of the industrial applications of AI in design, such as the needed steps to develop a successful AI-based tool, and a review of the Castlemain Project and telecommunication distribution networks follow. This book is suitable for programmers, computer science students, and architects and engineers who use computers in their line of work.




Advances in Computing and Information - ICCI '91


Book Description

This volume contains papers presented at the Third International Conference on Computing and Information, ICCI '91, held at Carleton University in Ottawa, Canada, May 27-29, 1991. The conference was organized by the School of Computer Science at Carleton University, and was sponsored by the Natural Sciences and Engineering Research Council of Canada (NSERC) and Carleton University. ICCI '91 was an international forum for the presentation of original results in research, development, and applications in computing and information processing. The conference was aimed at both practitioners and theoreticians, and was organized into five streams: - Algorithms and complexity, - Databases and information systems, - Parallel processing and systems, - Distributed computing and systems, - Expert systems, artificial intelligence. This volume contains three invited papers, by E.C.R. Hehner, R.L. Probert, and S.J. Smith, and 71 selected papers.




Pattern Recognition


Book Description

For more than 40 years, pattern recognition approaches are continuingly improving and have been used in an increasing number of areas with great success. This book discloses recent advances and new ideas in approaches and applications for pattern recognition. The 30 chapters selected in this book cover the major topics in pattern recognition. These chapters propose state-of-the-art approaches and cutting-edge research results. I could not thank enough to the contributions of the authors. This book would not have been possible without their support.




Support Vector Machines for Pattern Classification


Book Description

Support vector machines (SVMs), were originally formulated for two-class classification problems, and have been accepted as a powerful tool for developing pattern classification and function approximations systems. This book provides a unique perspective of the state of the art in SVMs by taking the only approach that focuses on classification rather than covering the theoretical aspects. The book clarifies the characteristics of two-class SVMs through their extensive analysis, presents various useful architectures for multiclass classification and function approximation problems, and discusses kernel methods for improving generalization ability of conventional neural networks and fuzzy systems. Ample illustrations, examples and computer experiments are included to help readers understand the new ideas and their usefulness. This book supplies a comprehensive resource for the use of SVMs in pattern classification and will be invaluable reading for researchers, developers & students in academia and industry.




Genetic Algorithms for Machine Learning


Book Description

The articles presented here were selected from preliminary versions presented at the International Conference on Genetic Algorithms in June 1991, as well as at a special Workshop on Genetic Algorithms for Machine Learning at the same Conference. Genetic algorithms are general-purpose search algorithms that use principles inspired by natural population genetics to evolve solutions to problems. The basic idea is to maintain a population of knowledge structure that represent candidate solutions to the problem of interest. The population evolves over time through a process of competition (i.e. survival of the fittest) and controlled variation (i.e. recombination and mutation). Genetic Algorithms for Machine Learning contains articles on three topics that have not been the focus of many previous articles on GAs, namely concept learning from examples, reinforcement learning for control, and theoretical analysis of GAs. It is hoped that this sample will serve to broaden the acquaintance of the general machine learning community with the major areas of work on GAs. The articles in this book address a number of central issues in applying GAs to machine learning problems. For example, the choice of appropriate representation and the corresponding set of genetic learning operators is an important set of decisions facing a user of a genetic algorithm. The study of genetic algorithms is proceeding at a robust pace. If experimental progress and theoretical understanding continue to evolve as expected, genetic algorithms will continue to provide a distinctive approach to machine learning. Genetic Algorithms for Machine Learning is an edited volume of original research made up of invited contributions by leading researchers.







Handbook of Constraint Programming


Book Description

Constraint programming is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from artificial intelligence, computer science, databases, programming languages, and operations research. Constraint programming is currently applied with success to many domains, such as scheduling, planning, vehicle routing, configuration, networks, and bioinformatics.The aim of this handbook is to capture the full breadth and depth of the constraint programming field and to be encyclopedic in its scope and coverage. While there are several excellent books on constraint programming, such books necessarily focus on the main notions and techniques and cannot cover also extensions, applications, and languages. The handbook gives a reasonably complete coverage of all these lines of work, based on constraint programming, so that a reader can have a rather precise idea of the whole field and its potential. Of course each line of work is dealt with in a survey-like style, where some details may be neglected in favor of coverage. However, the extensive bibliography of each chapter will help the interested readers to find suitable sources for the missing details. Each chapter of the handbook is intended to be a self-contained survey of a topic, and is written by one or more authors who are leading researchers in the area.The intended audience of the handbook is researchers, graduate students, higher-year undergraduates and practitioners who wish to learn about the state-of-the-art in constraint programming. No prior knowledge about the field is necessary to be able to read the chapters and gather useful knowledge. Researchers from other fields should find in this handbook an effective way to learn about constraint programming and to possibly use some of the constraint programming concepts and techniques in their work, thus providing a means for a fruitful cross-fertilization among different research areas.The handbook is organized in two parts. The first part covers the basic foundations of constraint programming, including the history, the notion of constraint propagation, basic search methods, global constraints, tractability and computational complexity, and important issues in modeling a problem as a constraint problem. The second part covers constraint languages and solver, several useful extensions to the basic framework (such as interval constraints, structured domains, and distributed CSPs), and successful application areas for constraint programming.- Covers the whole field of constraint programming- Survey-style chapters- Five chapters on applications