LEADER 07219nam 22008295 450 001 996465982503316 005 20221201185055.0 010 $a1-280-38945-1 010 $a9786613567376 010 $a3-642-16108-1 024 7 $a10.1007/978-3-642-16108-7 035 $a(CKB)2670000000045141 035 $a(SSID)ssj0000446287 035 $a(PQKBManifestationID)11299740 035 $a(PQKBTitleCode)TC0000446287 035 $a(PQKBWorkID)10495991 035 $a(PQKB)11718242 035 $a(DE-He213)978-3-642-16108-7 035 $a(MiAaPQ)EBC3065743 035 $a(PPN)149025211 035 $a(EXLCZ)992670000000045141 100 $a20100831d2010 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAlgorithmic Learning Theory$b[electronic resource] $e21st International Conference, ALT 2010, Canberra, Australia, October 6-8, 2010. Proceedings /$fedited by Marcus Hutter, Frank Stephan, Vladimir Vovk, Thomas Zeugmann 205 $a1st ed. 2010. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2010. 215 $a1 online resource (XIII, 421 p. 45 illus.) 225 1 $aLecture Notes in Artificial Intelligence ;$v6331 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-642-16107-3 320 $aIncludes bibliographical references and index. 327 $aEditors? Introduction -- Editors? Introduction -- Invited Papers -- Towards General Algorithms for Grammatical Inference -- The Blessing and the Curse of the Multiplicative Updates -- Discovery of Abstract Concepts by a Robot -- Contrast Pattern Mining and Its Application for Building Robust Classifiers -- Optimal Online Prediction in Adversarial Environments -- Regular Contributions -- An Algorithm for Iterative Selection of Blocks of Features -- Bayesian Active Learning Using Arbitrary Binary Valued Queries -- Approximation Stability and Boosting -- A Spectral Approach for Probabilistic Grammatical Inference on Trees -- PageRank Optimization in Polynomial Time by Stochastic Shortest Path Reformulation -- Inferring Social Networks from Outbreaks -- Distribution-Dependent PAC-Bayes Priors -- PAC Learnability of a Concept Class under Non-atomic Measures: A Problem by Vidyasagar -- A PAC-Bayes Bound for Tailored Density Estimation -- Compressed Learning with Regular Concept -- A Lower Bound for Learning Distributions Generated by Probabilistic Automata -- Lower Bounds on Learning Random Structures with Statistical Queries -- Recursive Teaching Dimension, Learning Complexity, and Maximum Classes -- Toward a Classification of Finite Partial-Monitoring Games -- Switching Investments -- Prediction with Expert Advice under Discounted Loss -- A Regularization Approach to Metrical Task Systems -- Solutions to Open Questions for Non-U-Shaped Learning with Memory Limitations -- Learning without Coding -- Learning Figures with the Hausdorff Metric by Fractals -- Inductive Inference of Languages from Samplings -- Optimality Issues of Universal Greedy Agents with Static Priors -- Consistency of Feature Markov Processes -- Algorithms for Adversarial Bandit Problems with Multiple Plays -- Online Multiple Kernel Learning: Algorithms and Mistake Bounds -- An Identity for Kernel Ridge Regression. 330 $aThis volume contains the papers presented at the 21st International Conference on Algorithmic Learning Theory (ALT 2010), which was held in Canberra, Australia, October 6?8, 2010. The conference was co-located with the 13th International Conference on Discovery Science (DS 2010) and with the Machine Learning Summer School, which was held just before ALT 2010. The technical program of ALT 2010, contained 26 papers selected from 44 submissions and five invited talks. The invited talks were presented in joint sessions of both conferences. ALT 2010 was dedicated to the theoretical foundations of machine learning and took place on the campus of the Australian National University, Canberra, Australia. ALT provides a forum for high-quality talks with a strong theoretical background and scientific interchange in areas such as inductive inference, universal prediction, teaching models, grammatical inference, formal languages, inductive logic programming, query learning, complexity of learning, on-line learning and relative loss bounds, semi-supervised and unsupervised learning, clustering, active learning, statistical learning, support vector machines, Vapnik- Chervonenkis dimension, probably approximately correct learning, Bayesian and causal networks, boosting and bagging, information-based methods, minimum descriptionlength,Kolmogorovcomplexity,kernels,graphlearning,decisiontree methods, Markov decision processes, reinforcement learning, and real-world - plications of algorithmic learning theory. DS 2010 was the 13th International Conference on Discovery Science and focused on the development and analysis of methods for intelligent data analysis, knowledge discovery and machine learning, as well as their application to scientific knowledge discovery. As is the tradition, it was co-located and held in parallel with Algorithmic Learning Theory. 410 0$aLecture Notes in Artificial Intelligence ;$v6331 606 $aArtificial intelligence 606 $aComputer programming 606 $aMathematical logic 606 $aAlgorithms 606 $aComputers 606 $aComputer logic 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 $aMathematical Logic and Formal Languages$3https://scigraph.springernature.com/ontologies/product-market-codes/I16048 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aComputation by Abstract Devices$3https://scigraph.springernature.com/ontologies/product-market-codes/I16013 606 $aLogics and Meanings of Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/I1603X 615 0$aArtificial intelligence. 615 0$aComputer programming. 615 0$aMathematical logic. 615 0$aAlgorithms. 615 0$aComputers. 615 0$aComputer logic. 615 14$aArtificial Intelligence. 615 24$aProgramming Techniques. 615 24$aMathematical Logic and Formal Languages. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aComputation by Abstract Devices. 615 24$aLogics and Meanings of Programs. 676 $a006.3/1 702 $aHutter$b Marcus$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aStephan$b Frank$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aVovk$b Vladimir$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZeugmann$b Thomas$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aALT 2010 906 $aBOOK 912 $a996465982503316 996 $aAlgorithmic Learning Theory$9771965 997 $aUNISA