LEADER 00000cam a2200793 a 4500 001 681311176 003 OCoLC 005 20240129213017.0 006 m o d 007 cr cnu---unuuu 008 101113s1990 caua ob 101 0 eng d 010 |z 90041088 019 755054338|a1048152580|a1064081973|a1086418912|a1112980051 |a1117506279|a1120893037|a1181908927|a1227636450 |a1269193159|a1277204367|a1281717923 020 9780323137706|q(electronic bk.) 020 0323137709|q(electronic bk.) 020 1299196551 020 9781299196551 029 1 AU@|b000051553643 029 1 AU@|b000056814658 029 1 DEBBG|bBV042308784 029 1 NZ1|b14777467 029 1 AU@|b000075792428 035 (OCoLC)681311176|z(OCoLC)755054338|z(OCoLC)1048152580 |z(OCoLC)1064081973|z(OCoLC)1086418912|z(OCoLC)1112980051 |z(OCoLC)1117506279|z(OCoLC)1120893037|z(OCoLC)1181908927 |z(OCoLC)1227636450|z(OCoLC)1269193159|z(OCoLC)1277204367 |z(OCoLC)1281717923 040 OCLCE|beng|epn|cOCLCE|dOCLCQ|dOPELS|dOCLCO|dOCLCQ|dOCLCF |dOCLCQ|dE7B|dOCLCQ|dUIU|dN$T|dYDXCP|dOCL|dOCLCQ|dVT2|dWYU |dHS0|dLEAUB|dUHL|dSFB|dLIP|dOCLCQ|dOCLCO|dUAB|dS2H|dOCLCO |dCOM|dOCLCO|dOCLCQ|dOCLCO|dOCLCL|dOCLCQ|dOCLCL 042 dlr 049 INap 082 04 511.3 082 04 511.3|223 099 eBook O’Reilly for Public Libraries 111 2 Workshop on Computational Learning Theory|n(3rd :|d1990 : |cRochester, N.Y.) 245 10 Proceedings of the Third Annual Workshop on Computational Learning Theory :|bUniversity of Rochester, Rochester, New York, August 6-8, 1990 /|csponsored by the ACM SIGACT/ SIGART ; [edited by] Mark Fulk, John Case.|h[O'Reilly electronic resource] 260 San Mateo, Calif. :|bMorgan Kaufmann Publishers,|c©1990. 300 1 online resource :|billustrations 336 text|btxt|2rdacontent 337 computer|bc|2rdamedia 338 online resource|bcr|2rdacarrier 504 Includes bibliographical references and index. 505 8 2 Stochastic Rules and Their Hierarchical Parameter Structures3 A Learning Criterion for Stochastic Rules -- A Stochastic PAC Model; 4 Hierarchical Learning Based on the MDL Principle; 5 The Optimality of MDL Rules and Their Convergence Rates; 6 Sample Complexity and Learnability of Stochastic Decision List Classes; 7 Concluding Remarks; References; Chapter 6. ON THE COMPLEXITY OF LEARNING MINIMUM TIME-BOUNDED TURING MACHINES; Abstract; 1. INTRODUCTION; 2. DEFINITIONS; 3. MAIN RESULTS; 4. PROOFS; 5. OPEN QUESTIONS; References; Chapter 7. INDUCTIVE INFERENCE FROM POSITIVE DATA IS POWERFUL 505 8 ABSTRACTINTRODUCTION; PRELIMINARIES; ELEMENTARY FORMAL SYSTEMS; INDUCTIVE INFERENCE FROM POSITIVE DATA; INDUCTIVE INFERENCE OF EFS MODELS FROM POSITIVE DATA; INDUCTIVE INFERENCE OF EFS LANGUAGES FROM POSITIVE DATA; DISCUSSION; Acknowledgments; References; Chapter 8. INDUCTIVE IDENTIFICATION OF PATTERN LANGUAGES WITH RESTRICTED SUBSTITUTIONS; ABSTRACT; PATTERN LANGUAGES OVER AN ARBITRARY BASE; PUMPING LEMMA; APPLICATION TO INDUCTIVE INFERENCE; References; Chapter 9. Pattern Languages Are Not Learnable; 1 Introduction; 2 PRELIMINAR IES; 3 The Main Result; Acknowledgments; References 506 |3Use copy|fRestrictions unspecified.|2star|5MiAaHDL 533 Electronic reproduction.|b[Place of publication not identified] :|cHathiTrust Digital Library,|d2010.|5MiAaHDL 538 Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002.|uhttp://purl.oclc.org/DLF/benchrepro0212 |5MiAaHDL 546 English. 583 1 digitized|c2010|hHathiTrust Digital Library|lcommitted to preserve|2pda|5MiAaHDL 588 0 Print version record. 590 O'Reilly|bO'Reilly Online Learning: Academic/Public Library Edition 650 0 Computational learning theory|vCongresses. 650 6 Théorie de l'apprentissage informatique|vCongrès. 650 7 MATHEMATICS|xGeneral.|2bisacsh 650 7 Computational learning theory|2fast 650 7 Apprentissage automatique|xCongrès.|2ram 655 2 Congress 655 7 proceedings (reports)|2aat 655 7 Conference papers and proceedings|2fast 655 7 Conference papers and proceedings.|2lcgft 655 7 Actes de congrès.|2rvmgf 700 1 Fulk, Mark A. 700 1 Case, John,|d1942-|1https://id.oclc.org/worldcat/entity/ E39PCjFkx9Vyfw8CGM3fB6h8Yd 710 2 ACM Special Interest Group for Automata and Computability Theory. 710 2 SIGART. 740 0 Colt '90. 740 0 Computational learning theory. 776 08 |iPrint version:|aWorkshop on Computational Learning Theory (3rd : 1990 : Rochester, N.Y.).|tProceedings of the Third Annual Workshop on Computational Learning Theory. |dSan Mateo, Calif. : Morgan Kaufmann Publishers, ©1990 |z9781558601468|w(DLC) 90041088|w(OCoLC)21972833 856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https:// learning.oreilly.com/library/view/~/9780323137706/?ar |zAvailable on O'Reilly for Public Libraries 938 ebrary|bEBRY|nebr10662635 938 EBSCOhost|bEBSC|n545173 938 YBP Library Services|bYANK|n10247594 994 92|bJFN