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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.

  • Computers and Languages

    Theory and Practice
    • 1st Edition
    • Volume 4
    • A. Nijholt
    • English
    A global introduction to language technology and the areas of computer science where language technology plays a role. Surveyed in this volume are issues related to the parsing problem in the fields of natural languages, programming languages, and formal languages.Throughout the book attention is paid to the social forces which influenced the development of the various topics. Also illustrated are the development of the theory of language analysis, its role in compiler construction, and its role in computer applications with a natural language interface between men and machine. Parts of the material in this book have been used in courses on computational linguistics, computers and society, and formal approaches to languages.

  • COLT '89

    Proceedings of the Second Annual Workshop, UC Santa Cruz, California, July 31 - August 2 1989
    • 1st Edition
    • COLT
    • English
    Computational Learning Theory presents the theoretical issues in machine learning and computational models of learning. This book covers a wide range of problems in concept learning, inductive inference, and pattern recognition. Organized into three parts encompassing 32 chapters, this book begins with an overview of the inductive principle based on weak convergence of probability measures. This text then examines the framework for constructing learning algorithms. Other chapters consider the formal theory of learning, which is learning in the sense of improving computational efficiency as opposed to concept learning. This book discusses as well the informed parsimonious (IP) inference that generalizes the compatibility and weighted parsimony techniques, which are most commonly applied in biology. The final chapter deals with the construction of prediction algorithms in a situation in which a learner faces a sequence of trials, with a prediction to be given in each and the goal of the learner is to make some mistakes. This book is a valuable resource for students and teachers.

  • Reasoning About Plans

    • 1st Edition
    • James Allen + 3 more
    • English
    This book presents four contributions to planning research within an integrated framework. James Allen offers a survey of his research in the field of temporal reasoning, and then describes a planning system formalized and implemented directly as an inference process in the temporal logic. Starting from the same logic, Henry Kautz develops the first formal specification of the plan recognition process and develops a powerful family of algorithms for plan recognition in complex situations. Richard Pelavin then extends the temporal logic with model operators that allow the representation to support reasoning about complex planning situations involving simultaneous interacting actions, and interaction with external events. Finally, Josh Tenenberg introduces two different formalisms of abstraction in planning systems and explores the properties of these abstraction techniques in depth.

  • Artificial Neural Networks and Statistical Pattern Recognition

    Old and New Connections
    • 1st Edition
    • Volume 11
    • I.K. Sethi + 1 more
    • English
    With the growing complexity of pattern recognition related problems being solved using Artificial Neural Networks, many ANN researchers are grappling with design issues such as the size of the network, the number of training patterns, and performance assessment and bounds. These researchers are continually rediscovering that many learning procedures lack the scaling property; the procedures simply fail, or yield unsatisfactory results when applied to problems of bigger size. Phenomena like these are very familiar to researchers in statistical pattern recognition (SPR), where the curse of dimensionality is a well-known dilemma. Issues related to the training and test sample sizes, feature space dimensionality, and the discriminatory power of different classifier types have all been extensively studied in the SPR literature. It appears however that many ANN researchers looking at pattern recognition problems are not aware of the ties between their field and SPR, and are therefore unable to successfully exploit work that has already been done in SPR. Similarly, many pattern recognition and computer vision researchers do not realize the potential of the ANN approach to solve problems such as feature extraction, segmentation, and object recognition. The present volume is designed as a contribution to the greater interaction between the ANN and SPR research communities.

  • Neural Networks

    Advances and Applications, 2
    • 1st Edition
    • E. Gelenbe
    • English
    The present volume is a natural follow-up to Neural Networks: Advances and Applications which appeared one year previously. As the title indicates, it combines the presentation of recent methodological results concerning computational models and results inspired by neural networks, and of well-documented applications which illustrate the use of such models in the solution of difficult problems. The volume is balanced with respect to these two orientations: it contains six papers concerning methodological developments and five papers concerning applications and examples illustrating the theoretical developments. Each paper is largely self-contained and includes a complete bibliography.The methodological part of the book contains two papers on learning, one paper which presents a computational model of intracortical inhibitory effects, a paper presenting a new development of the random neural network, and two papers on associative memory models. The applications and examples portion contains papers on image compression, associative recall of simple typed images, learning applied to typed images, stereo disparity detection, and combinatorial optimisation.

  • Introduction to Knowledge Systems

    • 1st Edition
    • 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.

  • Introduction to Machine Learning

    • 1st Edition
    • Yves Kodratoff
    • English
    A textbook suitable for undergraduate courses in machine learningand related topics, this book provides a broad survey of the field.Generous exercises and examples give students a firm grasp of theconcepts and techniques of this rapidly developing, challenging subject.Introduction to Machine Learning synthesizes and clarifiesthe work of leading researchers, much of which is otherwise availableonly in undigested technical reports, journals, and conference proceedings.Beginnin... with an overview suitable for undergraduate readers, Kodratoffestablishes a theoretical basis for machine learning and describesits technical concepts and major application areas. Relevant logicprogramming examples are given in Prolog.Introduction to Machine Learning is an accessible and originalintroduction to a significant research area.

  • Uncertainty in Artificial Intelligence

    Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, University of Washington, Seattle, July 29-31, 1994
    • 1st Edition
    • MKP
    • English
    Uncertainty in Artificial Intelligence: Proceedings of the Tenth Conference (1994) covers the papers accepted for presentation at the Tenth Annual Conference on Uncertainty in Artificial Intelligence, held in Seattle, Washington on July 29-31, 1994. The book focuses on the processes, methodologies, and approaches involved in artificial intelligence, including approximations, computational methods, Bayesian networks, and probabilistic inference. The selection first offers information on ending-based strategies for part-of-speech tagging; an evaluation of an algorithm for inductive learning of Bayesian belief networks using simulated data sets; and probabilistic constraint satisfaction with non-Gaussian noise. The text then examines Laplace's method approximations for probabilistic inference in belief networks with continuous variables; computational methods, bounds, and applications of counterfactual probabilities; and approximation algorithms for the loop cutset problem. The book takes a look at learning in multi-level stochastic games with delayed information; properties of Bayesian belief network learning algorithms; and the relation between kappa calculus and probabilistic reasoning. The manuscript also elaborates on intercausal independence and heterogeneous factorization; evidential reasoning with conditional belief functions; and state-space abstraction for anytime evaluation of probabilistic networks. The selection is a valuable reference for researches interested in artificial intelligence.

  • Mastering C Pointers

    Tools for Programming Power
    • 2nd Edition
    • Robert J. Traister
    • English
    If you don't fully understand C pointers and how they are used, you're not getting the most out of C programming. This book features complete coverage on using and controlling C language pointers to make C applications more powerful and expressive. This new edition is completely updated and revised to reflect the changes that have been brought about with the full adoption of ANSI C. All discussions and program examples have been updated, and reading materials necessary for any modern ANSI C programmer have also been added.Includes one 3 1/2" disk containing all of the working programs and modules found in the book.System Requirements: 286 or higher IBM PC or compatible.

  • Constraints, Language and Computation

    • 1st Edition
    • M. A. Rosner + 2 more
    • English
    Constraint-based linguistics is intersected by three fields: logic, linguistics, and computer sciences. The central theme that ties these different disciplines together is the notion of a linguistic formalism or metalanguage. This metalanguage has good mathematical properties, is designed to express descriptions of language, and has a semantics that can be implemented on a computer. Constraints, Language and Computation discusses the theory and practice of constraint-based computational linguistics. The book captures both the maturity of the field and some of its more interesting future prospects during a particulary important moment of development in this field.

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