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|a 9783540466505
|9 978-3-540-46650-5
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|a 10.1007/11894841
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|a Algorithmic Learning Theory
|h [electronic resource] :
|b 17th International Conference, ALT 2006, Barcelona, Spain, October 7-10, 2006, Proceedings /
|c edited by José L. Balcázar, Philip M. Long, Frank Stephan.
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| 250 |
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|a 1st ed. 2006.
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| 264 |
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2006.
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|a XIII, 393 p.
|b online resource.
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|a text
|b txt
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|b PDF
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|a Lecture Notes in Artificial Intelligence,
|x 2945-9141 ;
|v 4264
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| 505 |
0 |
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|a 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.
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| 650 |
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|a Artificial intelligence.
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| 650 |
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|a Computer science.
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| 650 |
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|a Algorithms.
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| 650 |
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|a Machine theory.
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| 650 |
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|a Natural language processing (Computer science).
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| 650 |
1 |
4 |
|a Artificial Intelligence.
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| 650 |
2 |
4 |
|a Theory of Computation.
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| 650 |
2 |
4 |
|a Algorithms.
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| 650 |
2 |
4 |
|a Formal Languages and Automata Theory.
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| 650 |
2 |
4 |
|a Natural Language Processing (NLP).
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| 700 |
1 |
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|a Balcázar, José L.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
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|a Long, Philip M.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
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|a Stephan, Frank.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 710 |
2 |
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|a SpringerLink (Online service)
|
| 773 |
0 |
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|t Springer Nature eBook
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| 776 |
0 |
8 |
|i Printed edition:
|z 9783540831693
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| 776 |
0 |
8 |
|i Printed edition:
|z 9783540466499
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| 830 |
|
0 |
|a Lecture Notes in Artificial Intelligence,
|x 2945-9141 ;
|v 4264
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| 856 |
4 |
0 |
|u https://doi.uam.elogim.com/10.1007/11894841
|z Texto Completo
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| 912 |
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|a ZDB-2-SCS
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| 912 |
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|a ZDB-2-SXCS
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| 912 |
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|a ZDB-2-LNC
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| 950 |
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|a Computer Science (SpringerNature-11645)
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| 950 |
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|a Computer Science (R0) (SpringerNature-43710)
|
