LEADER 04162nam 22007095 450 001 9910143880003321 005 20230401013222.0 010 $a3-540-36434-X 024 7 $a10.1007/3-540-36434-X 035 $a(CKB)1000000000211932 035 $a(SSID)ssj0000320845 035 $a(PQKBManifestationID)11231141 035 $a(PQKBTitleCode)TC0000320845 035 $a(PQKBWorkID)10262535 035 $a(PQKB)11563500 035 $a(DE-He213)978-3-540-36434-4 035 $a(MiAaPQ)EBC3072720 035 $a(PPN)155207857 035 $a(EXLCZ)991000000000211932 100 $a20121227d2003 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAdvanced Lectures on Machine Learning $eMachine Learning Summer School 2002, Canberra, Australia, February 11-22, 2002, Revised Lectures /$fedited by Shahar Mendelson, Alexander J. Smola 205 $a1st ed. 2003. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2003. 215 $a1 online resource (X, 266 p.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v2600 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-00529-3 320 $aIncludes bibliographical references and index. 327 $aA Few Notes on Statistical Learning Theory -- A Short Introduction to Learning with Kernels -- Bayesian Kernel Methods -- An Introduction to Boosting and Leveraging -- An Introduction to Reinforcement Learning Theory: Value Function Methods -- Learning Comprehensible Theories from Structured Data -- Algorithms for Association Rules -- Online Learning of Linear Classifiers. 330 $aMachine Learning has become a key enabling technology for many engineering applications and theoretical problems alike. To further discussions and to dis- minate new results, a Summer School was held on February 11?22, 2002 at the Australian National University. The current book contains a collection of the main talks held during those two weeks in February, presented as tutorial chapters on topics such as Boosting, Data Mining, Kernel Methods, Logic, Reinforcement Learning, and Statistical Learning Theory. The papers provide an in-depth overview of these exciting new areas, contain a large set of references, and thereby provide the interested reader with further information to start or to pursue his own research in these directions. Complementary to the book, a recorded video of the presentations during the Summer School can be obtained at http://mlg. anu. edu. au/summer2002 It is our hope that graduate students, lecturers, and researchers alike will ?nd this book useful in learning and teaching Machine Learning, thereby continuing the mission of the Summer School. Canberra, November 2002 Shahar Mendelson Alexander Smola Research School of Information Sciences and Engineering, The Australian National University Thanks and Acknowledgments We gratefully thank all the individuals and organizations responsible for the success of the workshop. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v2600 606 $aArtificial intelligence 606 $aSocial sciences 606 $aHumanities 606 $aComputer science 606 $aAlgorithms 606 $aArtificial Intelligence 606 $aHumanities and Social Sciences 606 $aTheory of Computation 606 $aAlgorithms 615 0$aArtificial intelligence. 615 0$aSocial sciences. 615 0$aHumanities. 615 0$aComputer science. 615 0$aAlgorithms. 615 14$aArtificial Intelligence. 615 24$aHumanities and Social Sciences. 615 24$aTheory of Computation. 615 24$aAlgorithms. 676 $a006.3/1 702 $aMendelson$b Shahar$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSmola$b Alexander J$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aMachine Learning Summer School 2002 906 $aBOOK 912 $a9910143880003321 996 $aAdvanced Lectures on Machine Learning$92018032 997 $aUNINA