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Algorithmic Learning Theory : 17th International Conference, ALT 2006, Barcelona, Spain, October 7-10, 2006, Proceedings / edited by José L. Balcázar, Philip M. Long, Frank Stephan
(Lecture Notes in Artificial Intelligence. ISSN:29459141 ; 4264)

データ種別	電子ブック
版	1st ed. 2006.
出版者	(Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer)
出版年	2006
大きさ	XIII, 393 p : online resource
著者標目	Balcázar, José L editor Long, Philip M editor Stephan, Frank editor SpringerLink (Online service)

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URL	巻　次	配架場所	請求記号	登録番号	コメント	刷　年	状　態	利用注記	ISBN	予約	請求メモ
URL		射水-電子	007	EB0004158	Computer Scinece R0 2005-6,2022-3				9783540466505

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一般注記	Editors’ Introduction -- Editors’ Introduction -- Invited Contributions -- Solving Semi-infinite Linear Programs Using Boosting-Like Methods -- e-Science and the Semantic Web: A Symbiotic Relationship -- Spectral Norm in Learning Theory: Some Selected Topics -- Data-Driven Discovery Using Probabilistic Hidden Variable Models -- Reinforcement Learning and Apprenticeship Learning for Robotic Control -- Regular Contributions -- Learning Unions of ?(1)-Dimensional Rectangles -- On Exact Learning Halfspaces with Random Consistent Hypothesis Oracle -- Active Learning in the Non-realizable Case -- How Many Query Superpositions Are Needed to Learn? -- Teaching Memoryless Randomized Learners Without Feedback -- The Complexity of Learning SUBSEQ (A) -- Mind Change Complexity of Inferring Unbounded Unions of Pattern Languages from Positive Data -- Learning and Extending Sublanguages -- Iterative Learning from Positive Data and Negative Counterexamples -- Towards a Better Understanding of Incremental Learning -- On Exact Learning from Random Walk -- Risk-Sensitive Online Learning -- Leading Strategies in Competitive On-Line Prediction -- Hannan Consistency in On-Line Learning in Case of Unbounded Losses Under Partial Monitoring -- General Discounting Versus Average Reward -- The Missing Consistency Theorem for Bayesian Learning: Stochastic Model Selection -- Is There an Elegant Universal Theory of Prediction? -- Learning Linearly Separable Languages -- Smooth Boosting Using an Information-Based Criterion -- Large-Margin Thresholded Ensembles for Ordinal Regression: Theory and Practice -- Asymptotic Learnability of Reinforcement Problems with Arbitrary Dependence -- Probabilistic Generalization of Simple Grammars and Its Application to Reinforcement Learning -- Unsupervised Slow Subspace-Learning from Stationary Processes -- Learning-Related Complexity of Linear Ranking Functions HTTP:URL=https://doi.org/10.1007/11894841
件　名	LCSH:Artificial intelligence LCSH:Computer science LCSH:Algorithms LCSH:Machine theory LCSH:Natural language processing (Computer science) FREE:Artificial Intelligence FREE:Theory of Computation FREE:Algorithms FREE:Formal Languages and Automata Theory FREE:Natural Language Processing (NLP)
分　類	LCC:Q334-342 LCC:TA347.A78 DC23:006.3
書誌ID	EB00003546
ISBN	9783540466505