Computational learning theory and natural learning systems / (Record no. 73166)

000 -LEADER
fixed length control field 04019nam a2200529 i 4500
001 - CONTROL NUMBER
control field 6267512
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220712204727.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 151228s1997 maua ob 001 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9780262291132
-- electronic : v. 4
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- v. l
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- print
082 00 - CLASSIFICATION NUMBER
Call Number 006.3/1
245 00 - TITLE STATEMENT
Title Computational learning theory and natural learning systems /
300 ## - PHYSICAL DESCRIPTION
Number of Pages 1 PDF (v. <1-4 >) :
500 ## - GENERAL NOTE
Remark 1 "A Bradford Book."
500 ## - GENERAL NOTE
Remark 1 Editors vary.
505 1# - FORMATTED CONTENTS NOTE
Remark 2 v. l. Constraints and prospects -- v. 2. Intersections between theory and experiment -- v. 3. Selecting good models -- v. 4. Making learning systems practical.
520 ## - SUMMARY, ETC.
Summary, etc 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.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
General subdivision Congresses.
700 1# - AUTHOR 2
Author 2 Hanson, Stephen Jos�e.
700 1# - AUTHOR 2
Author 2 Drastal, George A.
700 1# - AUTHOR 2
Author 2 Rivest, Ronald L.
856 42 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267512
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cambridge, Massachusetts :
-- MIT Press,
-- c1994-<c1997 >
264 #2 -
-- [Piscataqay, New Jersey] :
-- IEEE Xplore,
-- [1997]
336 ## -
-- text
-- rdacontent
337 ## -
-- electronic
-- isbdmedia
338 ## -
-- online resource
-- rdacarrier
588 ## -
-- Description based on PDF viewed 12/28/2015.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational learning theory

No items available.