Algorithmic Learning Theory 17th International Conference, ALT 2006, Barcelona, Spain, October 7-10, 2006, Proceedings /
Algorithmic Learning Theory 17th International Conference, ALT 2006, Barcelona, Spain, October 7-10, 2006, Proceedings / [electronic resource] :
edited by José L. Balcázar, Philip M. Long, Frank Stephan.
- 1st ed. 2006.
- XIII, 393 p. online resource.
- Lecture Notes in Artificial Intelligence, 4264 2945-9141 ; .
- Lecture Notes in Artificial Intelligence, 4264 .
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 fromStationary Processes -- Learning-Related Complexity of Linear Ranking Functions.
9783540466505
10.1007/11894841 doi
Artificial intelligence.
Computer science.
Algorithms.
Machine theory.
Natural language processing (Computer science).
Artificial Intelligence.
Theory of Computation.
Algorithms.
Formal Languages and Automata Theory.
Natural Language Processing (NLP).
Q334-342 TA347.A78
006.3
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 fromStationary Processes -- Learning-Related Complexity of Linear Ranking Functions.
9783540466505
10.1007/11894841 doi
Artificial intelligence.
Computer science.
Algorithms.
Machine theory.
Natural language processing (Computer science).
Artificial Intelligence.
Theory of Computation.
Algorithms.
Formal Languages and Automata Theory.
Natural Language Processing (NLP).
Q334-342 TA347.A78
006.3