Algorithmic Learning Theory: 10th International Conference, ALT’99 Tokyo, Japan, December 6–8, 1999 Proceedings
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...
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| Format: | Conference Paper |
| Language: | English |
| Published: |
Berlin, Heidelberg
Springer-Verlag Berlin Heidelberg
1999
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| Series: | Lecture notes in computer science
1720 |
| In: |
Lecture notes in computer science (1720)
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| Volumes / Articles: | Show Volumes / Articles. |
| DOI: | 10.1007/3-540-46769-6 |
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| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1007/3-540-46769-6 Cover: https://swbplus.bsz-bw.de/bsz322916186cov.jpg Inhaltsverzeichnis: https://swbplus.bsz-bw.de/bsz081974973inh.htm Kapitel 1: https://swbplus.bsz-bw.de/bsz081974973kap.htm Verlag, Zentralblatt MATH, Inhaltstext: https://zbmath.org/?q=an:0929.00070 
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| Author Notes: | edited by Osamu Watanabe, Takashi Yokomori |
| Summary: | 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 Unions of Pattern Languages and Tree Patterns. |
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| Physical Description: | Online Resource |
| ISBN: | 9783540467694 |
| DOI: | 10.1007/3-540-46769-6 |