Algorithmic learning theory : 17th international conference, ALT 2006, Barcelona, Spain, October 7-10, 2006 : proceedings / / Jose L. Balcazar, Philip M. Long, Frank Stephan, eds

• Record Nr.: UNINA9910483095403321
• Pubbl/distr/stampa: Berlin, : Springer, 2006
• ISBN: 3-540-46650-9
• Edizione: [1st ed. 2006.]
• Descrizione fisica: 1 online resource (XIII, 393 p.)
• Collana: Lecture notes in computer science. Lecture notes in artificial intelligence, , 0302-9743 ; ; 4264; LNCS sublibrary. SL 7, Artificial intelligence
• Altri autori (Persone): BalcazarJose Luis; LongPhilip M; StephanFrank (Frank Christian)
• Disciplina: 006.31
• Soggetti: Computer algorithms; Machine learning
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• 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.