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Books in Computer science

The Computing collection presents a range of foundational and applied content across computer and data science, including fields such as Artificial Intelligence; Computational Modelling; Computer Networks, Computer Organization & Architecture, Computer Vision & Pattern Recognition, Data Management; Embedded Systems & Computer Engineering; HCI/User Interface Design; Information Security; Machine Learning; Network Security; Software Engineering.

  • Introduction to Statistical Pattern Recognition

    • 2nd Edition
    • September 28, 1990
    • Keinosuke Fukunaga
    • English
    This completely revised second edition presents an introduction to statistical pattern recognition. Pattern recognition in general covers a wide range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology. Statistical decision and estimation, which are the main subjects of this book, are regarded as fundamental to the study of pattern recognition. This book is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field. Each chapter contains computer projects as well as exercises.

  • Algorithms and Complexity

    • 1st Edition
    • Volume A
    • September 12, 1990
    • Bozzano G Luisa
    • English
    This first part presents chapters on models of computation, complexity theory, data structures, and efficient computation in many recognized sub-disciplines of Theoretical Computer Science.

  • Advances in Electronics and Electron Physics

    • 1st Edition
    • Volume 78
    • August 30, 1990
    • English

  • Advances in Computers

    • 1st Edition
    • Volume 30
    • August 15, 1990
    • English

  • Minimalist Mobile Robotics

    • 1st Edition
    • July 28, 1990
    • Jonathan H. Connell
    • English
    Rather than using traditional artificial intelligence techniques, which are ineffective when applied to the complexities of real-world robot navigaiton, Connell describes a methodology of reconstructing intelligent robots with distributed, multiagent control systems. After presenting this methodology, hte author describes a complex, robust, and successful application-a mobile robot "can collection machine" which operates in an unmodified offifce environment occupied by moving people.

  • Decentralized A.I

    • 1st Edition
    • July 6, 1990
    • Y. Demazeau + 1 more
    • English
    Much research in Artificial Intelligence deals with a single agent having complete control over the world. A variation of this is Distributed AI (DAI), which is concerned with the collaborative solution of global problems by a distributed group of entities. This book deals with Decentralized AI (DzAI), which is concerned with the activity of an autonomous agent in a multi-agent world. The word ``agent'' is used in a broad sense, to designate an intelligent entity acting rationally and intentionally with respect to its goals and the current state of its knowledge. A number of these agents coexist and may collaborate with other agents in a common world; each agent may accomplish its own tasks, or cooperate with other agents to perform a personal or global task. The agents have imperfect knowledge about each other and about their common world, which they can update either through perception of the world, or by communication with each other.The papers were originally presented at a workshop held at King's College, Cambridge, and have been revised for this book.

  • Intelligent Mathematical Software Systems

    • 1st Edition
    • July 3, 1990
    • E.N. Houstis + 2 more
    • English
    Most of the well-known mathematical software systems are batch oriented, though in the past few years there have been attempts to incorporate ``knowledge'' or ``expertise'' into these systems. A number of developments have helped in making the systems more powerful and user-friendly: algorithm/parameter selection for the solution of well-defined mathematical engineering problems; parallel computing; computer graphics technology; interface development tools; and of course the years of experience with these systems and the increase in available computing power have made it practical to fulfill the potential seen in the early years of their development.This book covers four main areas of the subject: Application Oriented Expert Systems, Advisory Systems, Knowledge Manipulation Issues, and User Interfaces.

  • Matrix Perturbation Theory

    • 1st Edition
    • June 28, 1990
    • G. W. Stewart + 1 more
    • English
    This book is a comprehensive survey of matrix perturbation theory, a topic of interest to numerical analysts, statisticians, physical scientists, and engineers. In particular, the authors cover perturbation theory of linear systems and least square problems, the eignevalue problem, and the generalized eignevalue problem as wellas a complete treatment of vector and matrix norms, including the theory of unitary invariant norms.

  • Model Theory

    • 3rd Edition
    • Volume 73
    • June 12, 1990
    • C.C. Chang + 1 more
    • English
    Since the second edition of this book (1977), Model Theory has changed radically, and is now concerned with fields such as classification (or stability) theory, nonstandard analysis, model-theoretic algebra, recursive model theory, abstract model theory, and model theories for a host of nonfirst order logics. Model theoretic methods have also had a major impact on set theory, recursion theory, and proof theory.This new edition has been updated to take account of these changes, while preserving its usefulness as a first textbook in model theory. Whole new sections have been added, as well as new exercises and references. A number of updates, improvements and corrections have been made to the main text.

  • Computational Intelligence, II

    • 1st Edition
    • June 1, 1990
    • G. Mauri + 2 more
    • English
    The focus of this volume is ``Heterogeneous Knowledge and Problem Solving Integration'', i.e. the combined use of different knowledge representation and problem solving paradigms.This is a central topic for the design and implementation of problem solving systems, since, from a pragmatic and engineering standpoint, the solution of a large class of problems cannot take place within one single representation language or problem solving paradigm. Heterogeneous systems represent not only a pragmatic answer, but also a theoretical alternative to the homogeneous paradigms.

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