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181201 ||| eng |
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|a 9783540467694
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| 100 |
1 |
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|a Watanabe, Osamu
|e [editor]
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| 245 |
0 |
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|a Algorithmic Learning Theory
|h Elektronische Ressource
|b 10th International Conference, ALT '99 Tokyo, Japan, December 6-8, 1999 Proceedings
|c edited by Osamu Watanabe, Takashi Yokomori
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| 250 |
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|a 1st ed. 1999
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| 260 |
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|a Berlin, Heidelberg
|b Springer Berlin Heidelberg
|c 1999, 1999
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| 300 |
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|a XII, 372 p
|b online resource
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| 505 |
0 |
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|a Invited Lectures -- Tailoring Representations to Different Requirements -- Theoretical Views of Boosting and Applications -- Extended Stochastic Complexity and Minimax Relative Loss Analysis -- Regular Contributions -- Algebraic Analysis for Singular Statistical Estimation -- Generalization Error of Linear Neural Networks in Unidentifiable Cases -- The Computational Limits to the Cognitive Power of the Neuroidal Tabula Rasa -- The Consistency Dimension and Distribution-Dependent Learning from Queries (Extended Abstract) -- The VC-Dimension of Subclasses of Pattern Languages -- On the V ? Dimension for Regression in Reproducing Kernel Hilbert Spaces -- On the Strength of Incremental Learning -- Learning from Random Text -- Inductive Learning with Corroboration -- Flattening and Implication -- Induction of Logic Programs Based on ?-Terms -- Complexity in the Case Against Accuracy: When Building One Function-Free Horn Clause Is as Hard as Any -- A Method of Similarity-Driven Knowledge Revision for Type Specializations -- PAC Learning with Nasty Noise -- Positive and Unlabeled Examples Help Learning -- Learning Real Polynomials with a Turing Machine -- Faster Near-Optimal Reinforcement Learning: Adding Adaptiveness to the E3 Algorithm -- A Note on Support Vector Machine Degeneracy -- Learnability of Enumerable Classes of Recursive Functions from “Typical” Examples -- On the Uniform Learnability of Approximations to Non-recursive Functions -- Learning Minimal Covers of Functional Dependencies with Queries -- Boolean Formulas Are Hard to Learn for Most Gate Bases -- Finding Relevant Variables in PAC Model with Membership Queries -- General Linear Relations among Different Types of Predictive Complexity -- Predicting Nearly as Well as the Best Pruning of a Planar Decision Graph -- On Learning Unionsof Pattern Languages and Tree Patterns
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Algorithms
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| 653 |
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|a Formal Languages and Automata Theory
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| 653 |
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|a Machine theory
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| 653 |
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|a Artificial intelligence
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| 700 |
1 |
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|a Yokomori, Takashi
|e [editor]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 490 |
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|a Lecture Notes in Artificial Intelligence
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| 028 |
5 |
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|a 10.1007/3-540-46769-6
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| 856 |
4 |
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|u https://doi.org/10.1007/3-540-46769-6?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 006.3
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