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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.

BooksJournals

    • Artificial Neural Networks

      • 1st Edition
      • June 28, 2014
      • K. Mäkisara + 3 more
      • English
      • eBook
        9 7 8 1 4 8 3 2 9 8 0 0 9
      This two-volume proceedings compiles a selection of research papers presented at the ICANN-91. The scope of the volumes is interdisciplinary, ranging from mathematics and engineering to cognitive sciences and biology. European research is well represented. Volume 1 contains all the orally presented papers, including both invited talks and submitted papers. Volume 2 contains the plenary talks and the poster presentations.

    • Machine Learning

      • 1st Edition
      • June 28, 2014
      • Yves Kodratoff + 1 more
      • English
      • Hardback
        9 7 8 1 5 5 8 6 0 1 1 9 2
      • Paperback
        9 7 8 1 4 9 3 3 0 6 1 0 7
      • eBook
        9 7 8 0 0 8 0 5 1 0 5 5 2
      Machine Learning: An Artificial Intelligence Approach, Volume III presents a sample of machine learning research representative of the period between 1986 and 1989. The book is organized into six parts. Part One introduces some general issues in the field of machine learning. Part Two presents some new developments in the area of empirical learning methods, such as flexible learning concepts, the Protos learning apprentice system, and the WITT system, which implements a form of conceptual clustering. Part Three gives an account of various analytical learning methods and how analytic learning can be applied to various specific problems. Part Four describes efforts to integrate different learning strategies. These include the UNIMEM system, which empirically discovers similarities among examples; and the DISCIPLE multistrategy system, which is capable of learning with imperfect background knowledge. Part Five provides an overview of research in the area of subsymbolic learning methods. Part Six presents two types of formal approaches to machine learning. The first is an improvement over Mitchell's version space method; the second technique deals with the learning problem faced by a robot in an unfamiliar, deterministic, finite-state environment.

    • Case-Based Reasoning

      • 1st Edition
      • June 28, 2014
      • Janet Kolodner
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 2 3 7 3
      • eBook
        9 7 8 1 4 8 3 2 9 4 4 9 0
      Case-based reasoning is one of the fastest growing areas in the field of knowledge-based systems and this book, authored by a leader in the field, is the first comprehensive text on the subject. Case-based reasoning systems are systems that store information about situations in their memory. As new problems arise, similar situations are searched out to help solve these problems. Problems are understood and inferences are made by finding the closest cases in memory, comparing and contrasting the problem with those cases, making inferences based on those comparisons, and asking questions when inferences can't be made.This book presents the state of the art in case-based reasoning. The author synthesizes and analyzes a broad range of approaches, with special emphasis on applying case-based reasoning to complex real-world problem-solving tasks such as medical diagnosis, design, conflict resolution, and planning. The author's approach combines cognitive science and engineering, and is based on analysis of both expert and common-sense tasks. Guidelines for building case-based expert systems are provided, such as how to represent knowledge in cases, how to index cases for accessibility, how to implement retrieval processes for efficiency, and how to adapt old solutions to fit new situations. This book is an excellent text for courses and tutorials on case-based reasoning. It is also a useful resource for computer professionals and cognitive scientists interested in learning more about this fast-growing field.

    • Using C-Kermit

      • 1st Edition
      • June 28, 2014
      • Frank da Cruz + 1 more
      • English
      • Paperback
        9 7 8 1 5 5 5 5 8 1 0 8 4
      • eBook
        9 7 8 1 4 8 3 2 9 7 3 4 7
      An introduction and tutorial as well as a comprehensive reference Using C-Kermit describes the new release, 5A, of Columbia University's popular C-Kermit communication software - the most portable of all communication software packages. Available at low cost on a variety of magnetic media from Columbia University,C-Kermit can be used on computers of all sizes - ranging from desktop workstations to minicomputers to mainframes and supercomputers. The numerous examples, illustrations, and tables in Using C-Kermit make the powerful and versatile C-Kermit functionsaccessible for new and experienced users alike.

    • Machine Learning Proceedings 1989

      • 1st Edition
      • June 28, 2014
      • Alberto Maria Segre
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 0 3 6 2
      • eBook
        9 7 8 1 4 8 3 2 9 7 4 0 8
      Proceedings of the Sixth International Workshop on Machine Learning covers the papers presented at the Sixth International Workshop of Machine Learning, held at Cornell University, Ithaca, New York (USA) on June 26-27, 1989. The book focuses on the processes, methodologies, techniques, and approaches involved in machine learning. The selection first offers information on unifying themes in empirical and explanation-based learning; integrated learning of concepts with both explainable and conventional aspects; conceptual clustering of explanations; and tight integration of deductive and inductive learning. The text then examines multi-strategy learning in nonhomogeneous domain theories; description of preference criterion in constructive learning; and combining case-based reasoning, explanation-based learning, and learning from instruction. Discussions focus on causal explanation of actions, constructive learning, learning in a weak theory domain, learning problem, and individual criteria and their relationships. The book elaborates on learning from plausible explanations, augmenting domain theory for explanation-based generalization, reducing search and learning goal preferences, and using domain knowledge to improve inductive learning algorithms for diagnosis. The selection is a dependable reference for researchers interested in the dynamics of machine learning.

    • The Digital Guide To Software Development

      • 1st Edition
      • June 28, 2014
      • Christine Dickinson
      • English
      • Paperback
        9 7 8 1 5 5 5 5 8 0 3 5 3
      • eBook
        9 7 8 1 4 8 3 2 9 7 4 2 2
      Here is the first published description of the processes and practices, tools, and methods this industry giant uses to develop its software products. This 'shirt-sleeves' guide is packed with diagrams and tables that illustrate each step in the complexsoftware development process. You'll learn all about Digital's standard 'phase review process,' the role of teams and their leaders, how CASE tools work, and how to control a project while improving productivity and product quality.

    • Personal Computer Local Networks Report

      • 1st Edition
      • June 28, 2014
      • Architecture Technology Architecture Technology Corpor
      • English
      • Paperback
        9 7 8 1 8 5 6 1 7 0 9 3 2
      • eBook
        9 7 8 1 4 8 3 2 9 5 7 6 3
      Please note this is a Short Discount publication.Since the first microcomputer local networks of the late 1970's and early 80's, personal computer LANs have expanded in popularity, especially since the introduction of IBMs first PC in 1981. The late 1980s has seen a maturing in the industry with only a few vendors maintaining a large share of the market.This report is intended to give the reader a thorough understanding of the technology used to build these systems ... from cable to chips ... to ... protocols to servers. The report also fully defines PC LANs and the marketplace, with in–depth details on products, configurations, features, pricing, and service, plus lists of system components and features and vendor contact.

    • Computer Security in Financial Organizations

      • 1st Edition
      • June 28, 2014
      • J. Essinger
      • English
      • Paperback
        9 7 8 0 9 4 6 3 9 5 6 4 4
      • eBook
        9 7 8 1 4 8 3 2 9 4 6 2 9
      This book provides a unique in–depth focus on how financial organizations and suppliers of computer security are currently addressing – in strategic terms – the problem of computer security.Written in an easy to read, non technical style the book is essential reading for all those involved in the management of this sensitive area, from computer security managers, financial directors and managers to analysts and designers in financial software houses.The report analyses the computer security requirements of a wide variety of organizations in the financial services sector, ranging from retail, commercial and investment banks to financial trading and investment management organizations.

    • Introduction to Knowledge Systems

      • 1st Edition
      • June 28, 2014
      • Mark Stefik
      • English
      Focusing on fundamental scientific and engineering issues, this book communicates the principles of building and using knowledge systems from the conceptual standpoint as well as the practical. Previous treatments of knowledge systems have focused on applications within a particular field, or on symbol-level representations, such as the use of frame and rule representations. Introduction to Knowledge Systems presents fundamentals of symbol-level representations including representations for time, space, uncertainty, and vagueness. It also compares the knowledge-level organizations for three common knowledge-intensive tasks: classification, configuration, and diagnosis. The art of building knowledge systems incorporates computer science theory, programming practice, and psychology. The scope of this book is appropriately broad, ranging from the design of hierarchical search algorithms to techniques for acquiring the task-specific knowledge needed for successful applications. Each chapter proceeds from concepts to applications, and closes with a brief tour of current research topics and open issues. Readers will come away with a solid foundation that will enable them to create real-world knowledge systems using whatever tools and programming languages are most current and appropriate.

    • Machine Learning Proceedings 1994

      • 1st Edition
      • June 28, 2014
      • William W. Cohen
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 3 3 5 6
      • eBook
        9 7 8 1 4 8 3 2 9 8 1 8 4
      Machine Learning: Proceedings of the Eleventh International Conference covers the papers presented at the Eleventh International Conference on Machine Learning (ML94), held at New Brunswick, New Jersey on July 10-13, 1994. The book focuses on the processes, methodologies, and approaches involved in machine learning, including inductive logic programming, neural networks, and decision trees. The selection first offers information on learning recursive relations with randomly selected small training sets; improving accuracy of incorrect domain theories; and using sampling and queries to extract rules from trained neural networks. The text then takes a look at boosting and other machine learning algorithms; an incremental learning approach for completable planning; and learning disjunctive concepts by means of genetic algorithms. The publication ponders on rule induction for semantic query optimization; irrelevant features and the subset selection problem; and an efficient subsumption algorithm for inductive logic programming. The book also examines Bayesian inductive logic programming; a statistical approach to decision tree modeling; and an improved algorithm for incremental induction of decision trees. The selection is a dependable source of data for researchers interested in machine learning.

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