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Elsevier

Morgan Kaufmann

  • Economics-Driven Software Architecture

    • 1st Edition
    • Ivan Mistrik + 3 more
    • English
    Economics-driven Software Architecture presents a guide for engineers and architects who need to understand the economic impact of architecture design decisions: the long term and strategic viability, cost-effectiveness, and sustainability of applications and systems. Economics-driven software development can increase quality, productivity, and profitability, but comprehensive knowledge is needed to understand the architectural challenges involved in dealing with the development of large, architecturally challenging systems in an economic way. This book covers how to apply economic considerations during the software architecting activities of a project. Architecture-centric approaches to development and systematic evolution, where managing complexity, cost reduction, risk mitigation, evolvability, strategic planning and long-term value creation are among the major drivers for adopting such approaches. It assists the objective assessment of the lifetime costs and benefits of evolving systems, and the identification of legacy situations, where architecture or a component is indispensable but can no longer be evolved to meet changing needs at economic cost. Such consideration will form the scientific foundation for reasoning about the economics of nonfunctional requirements in the context of architectures and architecting.

  • Machine Learning Proceedings 1990

    Proceedings of the Seventh International Conference on Machine Learning, University of Texas, Austin, Texas, June 21-23 1990
    • 1st Edition
    • Bruce Porter + 1 more
    • English
    Machine Learning: Proceedings of the Seventh International Conference (1990) covers the research results from 12 disciplines of machine learning represented at the Seventh International Conference on Machine Learning, held on June 21-23, 1990 at the University of Texas in Austin. The book focuses on the progress in the interest in machine learning, including methodologies, approaches, and techniques. The selection first offers information on knowledge acquisition from examples using maximal representation learning, performance analysis of a probabilistic inductive learning system, and a comparative study of ID3 and backpropagation for English text-to-speech mapping. The text then examines learning from data with bounded inconsistency, improving fit-and-split algorithms, and an incremental method for finding multivariate splits for decision trees. Topics include issues for decision-tree induction, learning and approximation, conceptual-set-cover... algorithm, bounded inconsistency, implementation, and examples of incremental processes. The publication ponders on incremental induction of topologically minimal trees, rational analysis of categorization, search control, utility, and concept induction, graph clustering and model learning by data compression, and an analysis of representation shift in concept learning. Learning procedures by environment-driven constructive induction and improving the performance of genetic algorithms in automated discovery of parameters are also discussed. The selection is a valuable source of data for researchers interested in machine learning.

  • COLT '91

    Proceedings of the Fourth Annual Workshop, UC Santa Cruz, California, August 5-7, 1991
    • 1st Edition
    • COLT
    • English
    COLT '91: Proceedings of the Fourth Annual Workshop on Computational Learning Theory covers the papers presented at the Fourth Workshop on Computational Learning Theory, held at the University of California at Santa Cruz on August 5-7, 1991. The book focuses on quantitative theories of machine learning. The selection first offers information on the role of learning in autonomous robots; tracking drifting concepts using random examples; investigating the distribution assumptions in the PAC learning model; and simultaneous learning of concepts and simultaneous estimation of probabilities.The text then examines the calculation of the learning curve of Bayes optimal classification algorithm for learning a perceptron with noise and a geometric approach to threshold circuit complexity. The manuscript takes a look at learning curves in large neural networks, learnability of infinitary regular sets, and learning monotone DNF with an incomplete membership oracle. Topics include monotone DNF learning algorithm, difficulties in learning infinitary regular sets, learning of a perception rule, and annealed approximation. The book also examines the fast identification of geometric objects with membership queries and a loss bound model for on-line stochastic prediction strategies. The selection is a valuable source of information for researchers interested in the computational learning theory.

  • Distributed Artificial Intelligence

    Volume II
    • 1st Edition
    • Robin Gasser + 1 more
    • English
    Research Notes in Artificial Intelligence: Distributed Artificial Intelligence, Volume II focuses on the growing interest in Distributed Artificial Intelligence (DAI). The selection first offers information on a unified theory of communication and social structure and boundary objects and heterogeneous distributed problem solving. Discussions focus on types of boundary objects, heterogeneous problem solving and boundary objects, social structures and social groups, and social cooperation and communication. The text then examines representing and using organizational knowledge in DAI systems, dynamics of computational ecosystems, and communication-free interactions among rational agents. The publication takes a look at conflict-resolution strategies for nonhierarchical distributed agents, constraint-directed negotiation of resource reallocations, and plans for multiple agents. Topics include plan verification, generation, and execution, negotiation operators, representation, network management problem, and conflict-resolution paradigms. The manuscript then elaborates on negotiating task decomposition and allocation using partial global planning and mechanisms for assessing nonlocal impact of local decisions in distributed planning. The selection is a valuable source of information for researchers interested in distributed artificial intelligence.

  • Software Defined Networks

    A Comprehensive Approach
    • 1st Edition
    • Paul Goransson + 1 more
    • English
    Software Defined Networks discusses the historical networking environment that gave rise to SDN, as well as the latest advances in SDN technology. The book gives you the state of the art knowledge needed for successful deployment of an SDN, including: How to explain to the non-technical business decision makers in your organization the potential benefits, as well as the risks, in shifting parts of a network to the SDN model How to make intelligent decisions about when to integrate SDN technologies in a network How to decide if your organization should be developing its own SDN applications or looking to acquire these from an outside vendor How to accelerate the ability to develop your own SDN application, be it entirely novel or a more efficient approach to a long-standing problem

  • Machine Learning Proceedings 1988

    • 1st Edition
    • John Laird
    • English
    Proceedings of the Fifth International Conference on Machine Learning provides careful theoretical analyses that make clear contact with traditional problems in machine learning. This book discusses the key role of learning in cognition. Organized into 10 parts encompassing 49 chapters, this book begins with an overview of the OTIS induction system that learns concepts from positive and negative examples by searching through the space of possible concept descriptions. This text then reviews the methods to selecting examples, and explores the ramifications of one in detail. Other chapters consider a reported phenomenon in machine concept learning wherein concept descriptions can be simplified with little ill-effect on classification accuracy. This book discusses as well an implemented system that learns structural models of shape from noisy image data. The final chapter provides a discussion of the relationship between learning and forgetting. This book is a valuable resource for psychologists, scientists, theorists, and research workers.

  • Machine Learning Proceedings 1993

    Proceedings of the Tenth International Conference on Machine Learning, University of Massachusetts, Amherst, June 27-29, 1993
    • 1st Edition
    • Lawrence A. Birnbaum
    • English
    Machine Learning: Proceedings of the Tenth International Conference covers the papers presented at the Tenth International Conference on Machine Learning, held at Amherst, Massachusetts in June 27-29, 1993. The book focuses on the advancements of techniques, practices, approaches, and methodologies in machine learning. The selection first offers information on automatic algorithm/model class selection, using decision trees to improve case-based learning, GALOIS, and multitask learning. Discussions focus on multitask connectionist learning in more detail; multitask decision trees; an algorithm for the incremental determination of the concept lattice; and empirical evaluation of GALOIS as a learning system. The text then examines the use of qualitative models to guide inductive learning; automation of path analysis for building causal models from data; and construction of hidden variables in Bayesian networks via conceptual clustering. The book ponders on synthesis of abstraction hierarchies for constraint satisfaction by clustering approximately equivalent objects; efficient domain-independent experimentation; learning search control knowledge for deep space network scheduling; and learning procedures from interactive natural language instructions. The selection is a dependable reference for researchers wanting to explore the field of machine learning.

  • Database

    Principles Programming Performance
    • 1st Edition
    • Patrick O'Neil
    • English
    Database: Principles Programming Performance provides an introduction to the fundamental principles of database systems. This book focuses on database programming and the relationships between principles, programming, and performance. Organized into 10 chapters, this book begins with an overview of database design principles and presents a comprehensive introduction to the concepts used by a DBA. This text then provides grounding in many abstract concepts of the relational model. Other chapters introduce SQL, describing its capabilities and covering the statements and functions of the programming language. This book provides as well an introduction to Embedded SQL and Dynamic SQL that is sufficiently detailed to enable students to immediately start writing database programs. The final chapter deals with some of the motivations for database systems spanning multiple CPUs, including client-server and distributed transactions. This book is a valuable resource for database administrators, application programmers, specialist users, and end users.

  • Readings in Fuzzy Sets for Intelligent Systems

    • 1st Edition
    • Didier J. Dubois + 2 more
    • English
    Readings in Fuzzy Sets for Intelligent Systems is a collection of readings that explore the main facets of fuzzy sets and possibility theory and their use in intelligent systems. Basic notions in fuzzy set theory are discussed, along with fuzzy control and approximate reasoning. Uncertainty and informativeness, information processing, and membership, cognition, neural networks, and learning are also considered. Comprised of eight chapters, this book begins with a historical background on fuzzy sets and possibility theory, citing some forerunners who discussed ideas or formal definitions very close to the basic notions introduced by Lotfi Zadeh (1978). The reader is then introduced to fundamental concepts in fuzzy set theory, including symmetric summation and the setting of fuzzy logic; uncertainty and informativeness; and fuzzy control. Subsequent chapters deal with approximate reasoning; information processing; decision and management sciences; and membership, cognition, neural networks, and learning. Numerical methods for fuzzy clustering are described, and adaptive inference in fuzzy knowledge networks is analyzed. This monograph will be of interest to both students and practitioners in the fields of computer science, information science, applied mathematics, and artificial intelligence.

  • Exploring Artificial Intelligence

    Survey Talks from the National Conferences on Artificial Intelligence
    • 1st Edition
    • Howard E. Shrobe
    • English
    Exploring Artificial Intelligence: Survey Talks from the National Conference on Artificial Intelligence provides information pertinent to the distinct subareas of artificial intelligence research. This book discusses developments in machine learning techniques. Organized into six parts encompassing 16 chapters, this book begins with an overview of intelligent tutoring systems, which describes how to guide a student to learn new concepts. This text then links closely with one of the concerns of intelligent tutoring systems, namely how to interact through the utilization of natural language. Other chapters consider the various aspects of natural language understanding and survey the huge body of work that tries to characterize heuristic search programs. This book discusses as well how computer programs can create plans to satisfy goals. The final chapter deals with computational facilities that support. This book is a valuable resource for cognitive scientists, psychologists, domain experts, computer scientists, instructional designers, expert teachers, and research workers.

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