LEADER 01720oam 2200493zu 450 001 9910142334103321 005 20241212215420.0 010 $a9781538600528 010 $a1538600528 035 $a(CKB)1000000000036189 035 $a(SSID)ssj0000396496 035 $a(PQKBManifestationID)12138770 035 $a(PQKBTitleCode)TC0000396496 035 $a(PQKBWorkID)10465578 035 $a(PQKB)10972729 035 $a(EXLCZ)991000000000036189 100 $a20160829d2005 uy 101 0 $aeng 181 $ctxt 182 $cc 183 $acr 200 10$a18th International Conference on Systems Engineering : ICSEng 2005 : 16-18 August 2005, Las Vegas, Nevada : Proceedings 210 31$a[Place of publication not identified]$cIEEE Computer Society$d2005 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a9780769523590 311 08$a0769523595 606 $aSystems engineering$vCongresses 606 $aMechanical Engineering$2HILCC 606 $aEngineering & Applied Sciences$2HILCC 606 $aIndustrial & Management Engineering$2HILCC 615 0$aSystems engineering 615 7$aMechanical Engineering 615 7$aEngineering & Applied Sciences 615 7$aIndustrial & Management Engineering 676 $a620.001/171 702 $aSelvaraj$b Henry 702 $aMuthukumar$b Venkatesan 712 02$aUniversity of Nevada, Las Vegas 712 12$aIEEE International Conference on Systems Engineering 801 0$bPQKB 906 $aPROCEEDING 912 $a9910142334103321 996 $a18th International Conference on Systems Engineering : ICSEng 2005 : 16-18 August 2005, Las Vegas, Nevada : Proceedings$92411822 997 $aUNINA LEADER 04355nam 22007575 450 001 9910144348503321 005 20250610110201.0 010 $a3-540-28650-0 024 7 $a10.1007/b100712 035 $a(CKB)1000000000212566 035 $a(DE-He213)978-3-540-28650-9 035 $a(SSID)ssj0000097729 035 $a(PQKBManifestationID)11113219 035 $a(PQKBTitleCode)TC0000097729 035 $a(PQKBWorkID)10121045 035 $a(PQKB)10249743 035 $a(MiAaPQ)EBC3088047 035 $a(PPN)155207555 035 $a(MiAaPQ)EBC7252671 035 $a(EXLCZ)991000000000212566 100 $a20121227d2004 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Lectures on Machine Learning $eML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003, Revised Lectures /$fedited by Olivier Bousquet, Ulrike von Luxburg, Gunnar Rätsch 205 $a1st ed. 2004. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2004. 215 $a1 online resource (X, 246 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v3176 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a3-540-23122-6 320 $aIncludes bibliographical references and index. 327 $aAn Introduction to Pattern Classification -- Some Notes on Applied Mathematics for Machine Learning -- Bayesian Inference: An Introduction to Principles and Practice in Machine Learning -- Gaussian Processes in Machine Learning -- Unsupervised Learning -- Monte Carlo Methods for Absolute Beginners -- Stochastic Learning -- to Statistical Learning Theory -- Concentration Inequalities. 330 $aMachine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. 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