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Elsevier

Morgan Kaufmann

  • Foundations of Genetic Algorithms 1993 (FOGA 2)

    • 1st Edition
    • Volume 2
    • FOGA
    • English
    Foundations of Genetic Algorithms, Volume 2 provides insight of theoretical work in genetic algorithms. This book provides a general understanding of a canonical genetic algorithm. Organized into six parts encompassing 19 chapters, this volume begins with an overview of genetic algorithms in the broader adaptive systems context. This text then reviews some results in mathematical genetics that use probability distributions to characterize the effects of recombination on multiple loci in the absence of selection. Other chapters examine the static building block hypothesis (SBBH), which is the underlying assumption used to define deception. This book discusses as well the effect of noise on the quality of convergence of genetic algorithms. The final chapter deals with the primary goal in machine learning and artificial intelligence, which is to dynamically and automatically decompose problems into simpler problems to facilitate their solution. This book is a valuable resource for theorists and genetic algorithm researchers.

  • Readings in Groupware and Computer-Supported Cooperative Work

    Assisting Human-Human Collaboration
    • 1st Edition
    • Ronald M. Baecker
    • English
    This comprehensive introduction to the field represents the best of the published literature on groupware and computer-supported cooperative work (CSCW). The papers were chosen for their breadth of coverage of the field, their clarity of expression and presentation, their excellence in terms of technical innovation or behavioral insight, their historical significance, and their utility as sources for further reading. Taken as a whole, the papers and their introductions are a complete sourcebook to the field. This book will be useful for computer professionals involved in the development or purchase of groupware technology as well as for researchers and managers. It should also serve as a valuable text for university courses on CSCW, groupware, and human-computer interaction.

  • Intention-Based Diagnosis of Novice Programming Errors

    • 1st Edition
    • Lewis Johnson
    • English

  • Automated Generation of Model-Based Knowledge-Acquisition Tools

    • 1st Edition
    • Mark A. Musen
    • English

  • Parallelism in Production Systems

    • 1st Edition
    • Anoop Gupta
    • English

  • The Mathematics of Inheritance Systems

    • 1st Edition
    • David S. Touretzky
    • English

  • Parallelism and Programming in Classifier Systems

    • 1st Edition
    • Stephanie Forrest
    • English
    Parallelism and Programming in Classifier Systems deals with the computational properties of the underlying parallel machine, including computational completeness, programming and representation techniques, and efficiency of algorithms. In particular, efficient classifier system implementations of symbolic data structures and reasoning procedures are presented and analyzed in detail. The book shows how classifier systems can be used to implement a set of useful operations for the classification of knowledge in semantic networks. A subset of the KL-ONE language was chosen to demonstrate these operations. Specifically, the system performs the following tasks: (1) given the KL-ONE description of a particular semantic network, the system produces a set of production rules (classifiers) that represent the network; and (2) given the description of a new term, the system determines the proper location of the new term in the existing network. These two parts of the system are described in detail. The implementation reveals certain computational properties of classifier systems, including completeness, operations that are particularly natural and efficient, and those that are quite awkward. The book shows how high-level symbolic structures can be built up from classifier systems, and it demonstrates that the parallelism of classifier systems can be exploited to implement them efficiently. This is significant since classifier systems must construct large sophisticated models and reason about them if they are to be truly ""intelligent."" Parallel organizations are of interest to many areas of computer science, such as hardware specification, programming language design, configuration of networks of separate machines, and artificial intelligence This book concentrates on a particular type of parallel organization and a particular problem in the area of AI, but the principles that are elucidated are applicable in the wider setting of computer science.

  • Physically-Based Modeling for Computer Graphics

    A Structured Approach
    • 1st Edition
    • Ronen Barzel + 1 more
    • English
    Physically-Based Modeling for Computer Graphics: A Structured Approach addresses the challenge of designing and managing the complexity of physically-based models. This book will be of interest to researchers, computer graphics practitioners, mathematicians, engineers, animators, software developers and those interested in computer implementation and simulation of mathematical models.

  • Understanding the New SQL

    A Complete Guide
    • 1st Edition
    • Jim Melton + 1 more
    • English
    An effective introduction to SQL, and a comprehensive reference for years to come. As the editor of the 1992 standard, Jim Melton is an authority on the language and its new features. Using a highly readable, conversational style, he and Alan Simon clearly present the power of SQL. They describe practical methods of using SQL to solve problems, advanced SQL query expressions, dynamic SQL, transaction models, and database design.

  • C4.5

    Programs for Machine Learning
    • 1st Edition
    • J. Ross Quinlan
    • English
    Classifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged to have made some of the most significant contributions to their development. This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use , the source code (about 8,800 lines), and implementation notes. C4.5 starts with large sets of cases belonging to known classes. The cases, described by any mixture of nominal and numeric properties, are scrutinized for patterns that allow the classes to be reliably discriminated. These patterns are then expressed as models, in the form of decision trees or sets of if-then rules, that can be used to classify new cases, with emphasis on making the models understandable as well as accurate. The system has been applied successfully to tasks involving tens of thousands of cases described by hundreds of properties. The book starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting. Advantages and disadvantages of the C4.5 approach are discussed and illustrated with several case studies. This book should be of interest to developers of classification-based intelligent systems and to students in machine learning and expert systems courses.

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