LEADER 03485oam 2200517Ka 450 001 9910260612803321 005 20160803134623.0 035 $a(CKB)2670000000263681 035 $a(SSID)ssj0000127633 035 $a(PQKBManifestationID)11141906 035 $a(PQKBTitleCode)TC0000127633 035 $a(PQKBWorkID)10052384 035 $a(PQKB)10732070 035 $a(WaSeSS)Ind00065705 035 $a(OCoLC)827334738 035 $a(OCoLC-P)827334738 035 $a(MaCbMITP)2016 035 $a(PPN)25902158X 035 $a(EXLCZ)992670000000263681 100 $a20130212d1997 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 00$aComputational learning theory and natural learning systems 210 $aCambridge, Mass. ;$aLondon $cMIT Press 215 $a1 online resource (xxiii, 407 p.) 225 0 $aA Bradford Book 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-262-29113-4 320 $aIncludes bibliographical references and index. 330 $aThis 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.ContributorsKlaus 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 606 $aComputational learning theory$vCongresses 610 $aCOGNITIVE SCIENCES/General 615 0$aComputational learning theory 676 $a006.31 701 $aGreiner$b Russell$01252055 701 $aPetsche$b Thomas$01252056 701 $aHanson$b Stephen Jose?$01142888 801 0$bOCoLC-P 801 1$bOCoLC-P 906 $aBOOK 912 $a9910260612803321 996 $aComputational learning theory and natural learning systems$92902677 997 $aUNINA