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Elsevier

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.

  • Discrete and Continuous Boundary Problems

    • 1st Edition
    • Volume 8
    • Atkinson
    • English

  • Advances in Computers

    • 1st Edition
    • Volume 5
    • English
    The survey articles included in Volume 5 have been selected with two aims in mind: to arrive at a balanced sampling of the computer field, and to emphasize the subjects of most active current interest. Articles cover topics such as the role of computers in delivering election results; the state of computer development in the Soviet Union and its neighbors; artificial intelligence; and applications of computers to the design of optical instruments, to nuclear reactor design, and to the determination of the structure of crystals or molecules from X-ray diffraction patterns.

  • Advances in Electronics and Electron Physics

    • 1st Edition
    • Volume 19
    • English

  • Methods of Matrix Algebra

    • 1st Edition
    • Volume 16
    • Pease
    • English

  • Advances in Computers

    • 1st Edition
    • Volume 4
    • English

  • Differential-Difference Equations

    • 1st Edition
    • Volume 6
    • Bellman
    • English

  • Advances in Electronics and Electron Physics

    • 1st Edition
    • Volume 18
    • English

  • Advances in Electronics and Electron Physics

    • 1st Edition
    • Volume 17
    • English

  • Advances in Computers

    • 1st Edition
    • Volume 3
    • English

  • Optimization Techniques

    With Applications to Aerospace Systems
    • 1st Edition
    • Volume 5
    • George Leitmann
    • English
    During the past decade there has been a remarkable growth of interest in problems of systems optimization and of optimal control. And with this interest has come an increasing need for methods useful for rendering systems optimum. Rising to meet this challengc there have sprung up various “schools,” often championing onc method and regarding it superior to all others. Long experience has shown that life is not so simple, that the picture is not all white and black. In short, one may expect that a particular method is superior to others for the solution of some problems-rarely for all problems. Furthermore, since the basic mathematical formulation of optimization problems is often essentially the same in many approaches, it is not unreasonable to expect that there may be a great deal of similarity among various methods, a similarity - often, indeed, an identity-which is obscured by dissimilarities in language and notation. To help the uncommitted in his search for and rhoice of the optimum optimization technique is the fundamental aim of this volume. To accomplish this aim there are assembled in one book ten chapters dealing &h the various methods currently espoused for the solution of problems in systems optimization and optimal control. The choice of authors has been dictated solely by a consideration of an author’s interest and expertiless in a particular method. With the advantages of such an eclectic approach and the ensuing multiple authorship there comes some loss of smoothness of over - all presentation, for which the Editor must take the sole blame. On the one hand, correlation between the various chapters has been achieved by cross-referencing; on the other hand, each chapter can be read as a separate entity setting forth the technique championed by a particular “School.” While each of the ten chapters dealing with methods includes simple examples, primarily for didactic purposes, it has been thought useful to present four additional chapters dealing with applications alone. Of these, the first three, Chapters 11-13, cover specific optimization problems, and the final chapter contains a discussion of problems in the optimization of a complete system, in this case a nuclear propulsion system.

  • Advances in Electronics and Electron Physics

    • 1st Edition
    • Volume 14
    • English

  • Advances in Computers

    • 1st Edition
    • Volume 2
    • English

  • Advances in Electronics and Electron Physics

    • 1st Edition
    • Volume 15
    • English

  • Advances in Electronics and Electron Physics

    • 1st Edition
    • Volume 12
    • English

  • Advances in Electronics and Electron Physics

    • 1st Edition
    • Volume 13
    • English

  • Advances in Computers

    • 1st Edition
    • Volume 1
    • English

  • Advances in Electronics and Electron Physics

    • 1st Edition
    • Volume 11
    • English

  • Advances in Electronics and Electron Physics

    • 1st Edition
    • Volume 10
    • English

  • Advances in Electronics and Electron Physics

    • 1st Edition
    • Volume 9
    • English

  • Advances in Electronics and Electron Physics

    • 1st Edition
    • Volume 8
    • English

  • Advances in Electronics and Electron Physics

    • 1st Edition
    • Volume 7
    • English

  • Machine Learning

    An Artificial Intelligence Approach (Volume I)
    • 1st Edition
    • Ryszard S. Michalski + 2 more
    • English
    Machine Learning: An Artificial Intelligence Approach contains tutorial overviews and research papers representative of trends in the area of machine learning as viewed from an artificial intelligence perspective. The book is organized into six parts. Part I provides an overview of machine learning and explains why machines should learn. Part II covers important issues affecting the design of learning programs—particularl... programs that learn from examples. It also describes inductive learning systems. Part III deals with learning by analogy, by experimentation, and from experience. Parts IV and V discuss learning from observation and discovery, and learning from instruction, respectively. Part VI presents two studies on applied learning systems—one on the recovery of valuable information via inductive inference; the other on inducing models of simple algebraic skills from observed student performance in the context of the Leeds Modeling System (LMS). This book is intended for researchers in artificial intelligence, computer science, and cognitive psychology; students in artificial intelligence and related disciplines; and a diverse range of readers, including computer scientists, robotics experts, knowledge engineers, educators, philosophers, data analysts, psychologists, and electronic engineers.

  • Advances in Electronics and Electron Physics

    • 1st Edition
    • Volume 6
    • English

  • Advances in Electronics and Electron Physics

    • 1st Edition
    • Volume 5
    • English

  • Advances in Electronics and Electron Physics

    • 1st Edition
    • Volume 4
    • English

  • Advances in Electronics and Electron Physics

    • 1st Edition
    • Volume 3
    • English

  • Advances in Electronics and Electron Physics

    • 1st Edition
    • Volume 2
    • English

  • Advances in Electronics and Electron Physics

    • 1st Edition
    • Volume 1
    • English

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