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Author: George A. Drastal
Publisher:
ISBN:
Category : Computational learning theory
Languages : en
Pages :
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Author: George A. Drastal
Publisher:
ISBN:
Category : Computational learning theory
Languages : en
Pages :
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Author: Russell Greiner
Publisher: MIT Press
ISBN: 9780262571180
Category : Computational learning theory
Languages : en
Pages : 440
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Book Description
This is the fourth and final volume of papers from a series of workshops called "Computational Learning Theory and Ǹatural' Learning Systems." The purpose of the workshops was to explore the emerging intersection of theoretical learning research and natural learning systems. The workshops drew researchers from three historically distinct styles of learning research: computational learning theory, neural networks, and machine learning (a subfield of AI). Volume I of the series introduces the general focus of the workshops. Volume II looks at specific areas of interaction between theory and experiment. Volumes III and IV focus on key areas of learning systems that have developed recently. Volume III looks at the problem of "Selecting Good Models." The present volume, Volume IV, looks at ways of "Making Learning Systems Practical." The editors divide the twenty-one contributions into four sections. The first three cover critical problem areas: 1) scaling up from small problems to realistic ones with large input dimensions, 2) increasing efficiency and robustness of learning methods, and 3) developing strategies to obtain good generalization from limited or small data samples. The fourth section discusses examples of real-world learning systems. Contributors : Klaus Abraham-Fuchs, Yasuhiro Akiba, Hussein Almuallim, Arunava Banerjee, Sanjay Bhansali, Alvis Brazma, Gustavo Deco, David Garvin, Zoubin Ghahramani, Mostefa Golea, Russell Greiner, Mehdi T. Harandi, John G. Harris, Haym Hirsh, Michael I. Jordan, Shigeo Kaneda, Marjorie Klenin, Pat Langley, Yong Liu, Patrick M. Murphy, Ralph Neuneier, E.M. Oblow, Dragan Obradovic, Michael J. Pazzani, Barak A. Pearlmutter, Nageswara S.V. Rao, Peter Rayner, Stephanie Sage, Martin F. Schlang, Bernd Schurmann, Dale Schuurmans, Leon Shklar, V. Sundareswaran, Geoffrey Towell, Johann Uebler, Lucia M. Vaina, Takefumi Yamazaki, Anthony M. Zador.
Author: Stephen José Hanson
Publisher: Bradford Books
ISBN:
Category : Computers
Languages : en
Pages : 448
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Book Description
Volume I of the series introduces the general focus of the workshops. Volume II looks at specific areas of interaction between theory and experiment. Volumes III and IV focus on key areas of learning systems that have developed recently. Volume III looks at the problem of "Selecting Good Models." The present volume, Volume IV, looks at ways of "Making Learning Systems Practical." The editors divide the twenty-one contributions into four sections. The first three cover critical problem areas: 1) scaling up from small problems to realistic ones with large input dimensions, 2) increasing efficiency and robustness of learning methods, and 3) developing strategies to obtain good generalization from limited or small data samples. The fourth section discusses examples of real-world learning systems.
Author: Ashwin Ram
Publisher: MIT Press
ISBN: 9780262181655
Category : Computers
Languages : en
Pages : 548
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Brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process. In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learner's goals. The fundamental tenet of goal-driven learning is that learning is largely an active and strategic process in which the learner, human or machine, attempts to identify and satisfy its information needs in the context of its tasks and goals, its prior knowledge, its capabilities, and environmental opportunities for learning. This book brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process. It collects and solidifies existing results on this important issue in machine and human learning and presents a theoretical framework for future investigations. The book opens with an an overview of goal-driven learning research and computational and cognitive models of the goal-driven learning process. This introduction is followed by a collection of fourteen recent research articles addressing fundamental issues of the field, including psychological and functional arguments for modeling learning as a deliberative, planful process; experimental evaluation of the benefits of utility-based analysis to guide decisions about what to learn; case studies of computational models in which learning is driven by reasoning about learning goals; psychological evidence for human goal-driven learning; and the ramifications of goal-driven learning in educational contexts. The second part of the book presents six position papers reflecting ongoing research and current issues in goal-driven learning. Issues discussed include methods for pursuing psychological studies of goal-driven learning, frameworks for the design of active and multistrategy learning systems, and methods for selecting and balancing the goals that drive learning. A Bradford Book
Author: Angel P. del Pobil
Publisher: Springer
ISBN: 3540693483
Category : Computers
Languages : en
Pages : 911
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Book Description
This two-volume set constitutes the refereed proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE-98, held in Benicassim, Castellon, Spain, in June 1998.The two volumes present a total of 187 revised full papers selected from 291 submissions. In accordance with the conference, the books are devoted to new methodologies, knowledge modeling and hybrid techniques. The papers explore applications from virtually all subareas of AI including knowledge-based systems, fuzzyness and uncertainty, formal reasoning, neural information processing, multiagent systems, perception, robotics, natural language processing, machine learning, supervision and control systems, etc..
Author: Michael S Schuurman
Publisher: World Scientific
ISBN: 9814397938
Category : Science
Languages : en
Pages : 769
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Book Description
The concept of adiabatic electronic potential-energy surfaces, defined by the Born-Oppenheimer approximation, is fundamental to our thinking about chemical processes. Recent computational as well as experimental studies have produced ample evidence that the so-called conical intersections of electronic energy surfaces, predicted by von Neumann and Wigner in 1929, are the rule rather than the exception in polyatomic molecules. It is nowadays increasingly recognized that conical intersections play a key mechanistic role in chemical reaction dynamics. This volume provides an up-to-date overview of the multi-faceted research on the role of conical intersections in photochemistry and photobiology, including basic theoretical concepts, novel computational strategies as well as innovative experiments. The contents and discussions will be of value to advanced students and researchers in photochemistry, molecular spectroscopy and related areas.
Author:
Publisher:
ISBN:
Category : Libraries
Languages : en
Pages : 864
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Book Description
Includes, beginning Sept. 15, 1954 (and on the 15th of each month, Sept.-May) a special section: School library journal, ISSN 0000-0035, (called Junior libraries, 1954-May 1961). Also issued separately.
Author:
Publisher:
ISBN:
Category : American literature
Languages : en
Pages : 2576
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Author: Stephen José Hanson
Publisher: Mit Press
ISBN: 9780262581332
Category : Computers
Languages : en
Pages : 449
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Book Description
Annotation These original contributions converge on an exciting and fruitful intersection of three historically distinct areas of learning research: computational learning theory, neural networks, and symbolic machine learning. Bridging theory and practice, computer science and psychology, they consider general issues in learning systems that could provide constraints for theory and at the same time interpret theoretical results in the context of experiments with actual learning systems. In all, nineteen chapters address questions such as, What is a natural system? How should learning systems gain from prior knowledge? If prior knowledge is important, how can we quantify how important? What makes a learning problem hard? How are neural networks and symbolic machine learning approaches similar? Is there a fundamental difference in the kind of task a neural network can easily solve as opposed to those a symbolic algorithm can easily solve? Stephen J. Hanson heads the Learning Systems Department at Siemens Corporate Research and is a Visiting Member of the Research Staff and Research Collaborator at the Cognitive Science Laboratory at Princeton University. George A. Drastal is Senior Research Scientist at Siemens Corporate Research. Ronald J. Rivest is Professor of Computer Science and Associate Director of the Laboratory for Computer Science at the Massachusetts Institute of Technology.
Author:
Publisher:
ISBN:
Category : Academic libraries
Languages : en
Pages : 716
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