LEADER 06178nam 22008535 450 001 996465309803316 005 20200704101027.0 010 $a3-642-04414-X 024 7 $a10.1007/978-3-642-04414-4 035 $a(CKB)1000000000784698 035 $a(SSID)ssj0000316161 035 $a(PQKBManifestationID)11242656 035 $a(PQKBTitleCode)TC0000316161 035 $a(PQKBWorkID)10263698 035 $a(PQKB)10051989 035 $a(DE-He213)978-3-642-04414-4 035 $a(MiAaPQ)EBC3064617 035 $a(PPN)139955674 035 $a(EXLCZ)991000000000784698 100 $a20100301d2009 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAlgorithmic Learning Theory$b[electronic resource] $e20th International Conference, ALT 2009, Porto, Portugal, October 3-5, 2009, Proceedings /$fedited by Ricard Gavaldą, Gabor Lugosi, Thomas Zeugmann, Sandra Zilles 205 $a1st ed. 2009. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2009. 215 $a1 online resource (XI, 399 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v5809 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-642-04413-1 320 $aIncludes bibliographical references and index. 327 $aInvited Papers -- The Two Faces of Active Learning -- Inference and Learning in Planning -- Mining Heterogeneous Information Networks by Exploring the Power of Links -- Learning and Domain Adaptation -- Learning on the Web -- Regular Contributions -- Prediction with Expert Evaluators? Advice -- Pure Exploration in Multi-armed Bandits Problems -- The Follow Perturbed Leader Algorithm Protected from Unbounded One-Step Losses -- Computable Bayesian Compression for Uniformly Discretizable Statistical Models -- Calibration and Internal No-Regret with Random Signals -- St. Petersburg Portfolio Games -- Reconstructing Weighted Graphs with Minimal Query Complexity -- Learning Unknown Graphs -- Completing Networks Using Observed Data -- Average-Case Active Learning with Costs -- Canonical Horn Representations and Query Learning -- Learning Finite Automata Using Label Queries -- Characterizing Statistical Query Learning: Simplified Notions and Proofs -- An Algebraic Perspective on Boolean Function Learning -- Adaptive Estimation of the Optimal ROC Curve and a Bipartite Ranking Algorithm -- Complexity versus Agreement for Many Views -- Error-Correcting Tournaments -- Difficulties in Forcing Fairness of Polynomial Time Inductive Inference -- Learning Mildly Context-Sensitive Languages with Multidimensional Substitutability from Positive Data -- Uncountable Automatic Classes and Learning -- Iterative Learning from Texts and Counterexamples Using Additional Information -- Incremental Learning with Ordinal Bounded Example Memory -- Learning from Streams -- Smart PAC-Learners -- Approximation Algorithms for Tensor Clustering -- Agnostic Clustering. 330 $aThis book constitutes the refereed proceedings of the 20th International Conference on Algorithmic Learning Theory, ALT 2009, held in Porto, Portugal, in October 2009, co-located with the 12th International Conference on Discovery Science, DS 2009. The 26 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from 60 submissions. The papers are divided into topical sections of papers on online learning, learning graphs, active learning and query learning, statistical learning, inductive inference, and semisupervised and unsupervised learning. The volume also contains abstracts of the invited talks: Sanjoy Dasgupta, The Two Faces of Active Learning; Hector Geffner, Inference and Learning in Planning; Jiawei Han, Mining Heterogeneous; Information Networks By Exploring the Power of Links, Yishay Mansour, Learning and Domain Adaptation; Fernando C.N. Pereira, Learning on the Web. 410 0$aLecture Notes in Artificial Intelligence ;$v5809 606 $aArtificial intelligence 606 $aComputer programming 606 $aData mining 606 $aNatural language processing (Computer science) 606 $aPattern recognition 606 $aInformation storage and retrieval 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aProgramming Techniques$3https://scigraph.springernature.com/ontologies/product-market-codes/I14010 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aNatural Language Processing (NLP)$3https://scigraph.springernature.com/ontologies/product-market-codes/I21040 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 608 $aKongress.$2swd 608 $aPorto (Portugal, 2009)$2swd 615 0$aArtificial intelligence. 615 0$aComputer programming. 615 0$aData mining. 615 0$aNatural language processing (Computer science). 615 0$aPattern recognition. 615 0$aInformation storage and retrieval. 615 14$aArtificial Intelligence. 615 24$aProgramming Techniques. 615 24$aData Mining and Knowledge Discovery. 615 24$aNatural Language Processing (NLP). 615 24$aPattern Recognition. 615 24$aInformation Storage and Retrieval. 676 $a006.3/1 686 $aDAT 708f$2stub 686 $aSS 4800$2rvk 702 $aGavaldą$b Ricard$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLugosi$b Gabor$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZeugmann$b Thomas$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZilles$b Sandra$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aALT 2009 906 $aBOOK 912 $a996465309803316 996 $aAlgorithmic Learning Theory$9771965 997 $aUNISA