Algorithmic Learning Theory [[electronic resource] ] : 17th International Conference, ALT 2006, Barcelona, Spain, October 7-10, 2006, Proceedings / / edited by José L. Balcázar, Philip M. Long, Frank Stephan

• Pubblicazione: Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006
• Edizione: 1st ed. 2006.
• Descrizione fisica: 1 online resource (XIII, 393 p.)
• Disciplina: 006.31
• Soggetto topico: Artificial intelligence; Computers; Algorithms; Mathematical logic; Natural language processing (Computer science); Artificial Intelligence; Computation by Abstract Devices; Algorithm Analysis and Problem Complexity; Mathematical Logic and Formal Languages; Natural Language Processing (NLP)
• Persona (resp. second.): BalcázarJosé L; LongPhilip M; StephanFrank
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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.
• Titolo autorizzato: Algorithmic Learning Theory
• ISBN: 3-540-46650-9
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996466021703316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture Notes in Artificial Intelligence ; ; 4264