LEADER 06430nam 22008055 450 001 9910484805303321 005 20251226195600.0 010 $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 $e21st International Conference, ALT 2010, Canberra, Australia, October 6-8, 2010. Proceedings /$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 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v6331 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$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 KernelLearning: Algorithms and Mistake Bounds -- An Identity for Kernel Ridge Regression. 330 $aThis volume contains the papers presented at the 21st International Conf- ence on Algorithmic Learning Theory (ALT 2010), which was held in Canberra, Australia, October 6?8, 2010. The conference was co-located with the 13th - ternational Conference on Discovery Science (DS 2010) and with the Machine Learning Summer School, which was held just before ALT 2010. The tech- cal program of ALT 2010, contained 26 papers selected from 44 submissions and ?ve invited talks. The invited talks were presented in joint sessions of both conferences. ALT 2010 was dedicated to the theoretical foundations of machine learning and took place on the campus of the Australian National University, Canberra, Australia. ALT provides a forum for high-quality talks with a strong theore- cal background and scienti?c interchange in areas such as inductive inference, universal prediction, teaching models, grammatical inference, formal languages, inductive logic programming, query learning, complexity of learning, on-line learning and relative loss bounds, semi-supervised and unsupervised learning, clustering,activelearning,statisticallearning,supportvectormachines,Vapnik- Chervonenkisdimension,probablyapproximatelycorrectlearning,Bayesianand causal networks, boosting and bagging, information-based methods, minimum descriptionlength,Kolmogorovcomplexity,kernels,graphlearning,decisiontree methods, Markov decision processes, reinforcement learning, and real-world - plications of algorithmic learning theory. DS 2010 was the 13th International Conference on Discovery Science and focused on the development and analysis of methods for intelligent data an- ysis, knowledge discovery and machine learning, as well as their application to scienti?c knowledge discovery. As is the tradition, it wasco-located and held in parallel with Algorithmic Learning Theory. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v6331 606 $aArtificial intelligence 606 $aComputer programming 606 $aMachine theory 606 $aAlgorithms 606 $aComputer science 606 $aArtificial Intelligence 606 $aProgramming Techniques 606 $aFormal Languages and Automata Theory 606 $aAlgorithms 606 $aTheory of Computation 606 $aComputer Science Logic and Foundations of Programming 615 0$aArtificial intelligence. 615 0$aComputer programming. 615 0$aMachine theory. 615 0$aAlgorithms. 615 0$aComputer science. 615 14$aArtificial Intelligence. 615 24$aProgramming Techniques. 615 24$aFormal Languages and Automata Theory. 615 24$aAlgorithms. 615 24$aTheory of Computation. 615 24$aComputer Science Logic and Foundations of Programming. 676 $a006.3/1 701 $aHutter$b Marcus$01752669 712 12$aALT 2010 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484805303321 996 $aAlgorithmic learning theory$94188030 997 $aUNINA