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Morgan Kaufmann

  • Organizing Information

    Principles of Data Base and Retrieval Systems
    • 1st Edition
    • September 26, 1985
    • Dagobert Soergel
    • English
    This book gives a theoretical base and a perspective for the analysis, design, and operation of information systems, particularly their information storage and retrieval (ISAR) component, whether mechanized or manual. Information systems deal with many types of entities: events, persons, documents, business transactions, museum objects, research projects, and technical parts, to name a few. Among the purposes the serve are to inform the public, to support managers, researchers, and engineers, and to provide a knowledge base for an artificial intelligence program. The principles discussed in this book apply to all these contexts. The book achieves this generality by drawing on ideas from two conceptually overlapping areas—data base management and the organization and use of knowledge in libraries—and by integrating these ideas into a coherent framework. The principles discussed apply to the design of new systems and, more importantly, to the analysis of existing systems in order to exploit their capabilities better, to circumvent their shortcomings, and to introduce modifications where feasible.

  • Readings in Knowledge Representation

    • 1st Edition
    • January 1, 1985
    • Ronald J. Brachman + 1 more
    • English

  • Proceedings 1983 VLDB Conference

    9th International Conference on Very Large Data Bases
    • 1st Edition
    • December 1, 1983
    • VLDB
    • English

  • IJCAI Proceedings 1979

    • 1st Edition
    • December 25, 1979
    • IJCAI
    • English

  • Machine Learning

    An Artificial Intelligence Approach (Volume I)
    • 1st Edition
    • January 1, 1955
    • 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.

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