Algorithmic learning theory : 11th International Conference, ALT 2000, Sydney, Australia, December 11 13, 2000 : proceedings
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| Format: | Livre numérique |
| Język: | Anglais |
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Berlin [etc.] :
Springer
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Cham : Springer Nature |
| Seria: | Lecture notes in computer science. Lecture notes in artificial intelligence
1968 |
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| Edition sous un autre format: | • Algorithmic learning theory, 11th International Conference, ALT 2000 Sydney, Australia, December 2000, proceedings, Hiroki Arimura, Sanjay Jain, Arun Sharma (eds.), 2000, Berlin, Springer, 1 vol. (XI-333 p.), Lecture notes in computer science, 3-540-41237-9 • Algorithmic Learning Theory, Texte imprimé, 9783662195215 |
Spis treści:
- INVITED LECTURES
- Extracting Information from the Web for Concept Learning and Collaborative Filtering
- The Divide-and-Conquer Manifesto
- Sequential Sampling Techniques for Algorithmic Learning Theory
- REGULAR CONTRIBUTIONS
- Towards an Algorithmic Statistics
- Minimum Message Length Grouping of Ordered Data
- Learning From Positive and Unlabeled Examples
- Learning Erasing Pattern Languages with Queries
- Learning Recursive Concepts with Anomalies
- Identification of Function Distinguishable Languages
- A Probabilistic Identification Result
- A New Framework for Discovering Knowledge from Two-Dimensional Structured Data Using Layout Formal Graph System
- Hypotheses Finding via Residue Hypotheses with the Resolution Principle
- Conceptual Classifications Guided by a Concept Hierarchy
- Learning Taxonomic Relation by Case-based Reasoning
- Average-Case Analysis of Classification Algorithms for Boolean Functions and Decision Trees
- Self-duality of Bounded Monotone Boolean Functions and Related Problems
- Sharper Bounds for the Hardness of Prototype and Feature Selection
- On the Hardness of Learning Acyclic Conjunctive Queries
- Dynamic Hand Gesture Recognition Based On Randomized Self-Organizing Map Algorithm
- On Approximate Learning by Multi-layered Feedforward Circuits
- The Last-Step Minimax Algorithm
- Rough Sets and Ordinal Classification
- A note on the generalization performance of kernel classifiers with margin
- On the Noise Model of Support Vector Machines Regression
- Computationally Efficient Transductive Machines.