Computational learning theory and natural learning systems / edited by Stephen J. Hanson, George A. Drastal, and Ronald L. Rivest.
Contributor(s): Hanson, Stephen Jos�e | Drastal, George A | Rivest, Ronald L | IEEE Xplore (Online Service) [distributor.] | MIT Press [publisher.].
Material type:
BookPublisher: Cambridge, Massachusetts : MIT Press, c1994-<c1997 >Distributor: [Piscataqay, New Jersey] : IEEE Xplore, [1997]Description: 1 PDF (v. <1-4 >) : illustrations.Content type: text Media type: electronic Carrier type: online resourceISBN: 9780262291132.Subject(s): Computational learning theory -- CongressesGenre/Form: Electronic books.DDC classification: 006.3/1 Online resources: Abstract with links to resource Also available in print."A Bradford Book."
Editors vary.
Includes bibliographical references and indexes.
v. l. Constraints and prospects -- v. 2. Intersections between theory and experiment -- v. 3. Selecting good models -- v. 4. Making learning systems practical.
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This is the fourth and final volume of papers from a series of workshops called "Computational Learning Theory and `Natural' 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.
Also available in print.
Mode of access: World Wide Web
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