Algorithmic learning theory : 12th international conference, ALT 2001, Washington, DC, USA, November 25-28, 2001 : proceedings
This volume contains the papers presented at the 12th Annual Conference on Algorithmic Learning Theory (ALT 2001), which was held in Washington DC, USA, during November 25 28, 2001. The main objective of the conference is to provide an inter-disciplinary forum for the discussion of theoretical found...
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| Autres auteurs: | , , |
| Format: | Livre numérique |
| Langue: | Anglais |
| Publié: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Collection: | Lecture notes in computer science. Lecture notes in artificial intelligence
2225 |
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| Accès en ligne: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Note: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Algorithmic learning theory, 12th international conference, ALT 2001, Washington, DC, USA, November 25-28, 2001, proceedings, Naoki Abe, Roni Khardon, Thomas Zeugmann (eds.), 2001, New York, Springer, 1 vol. (XI-377 p.), Lecture notes in computer science, 3-540-42875-5 • Algorithmic Learning Theory, Texte imprimé, 9783662215340 |
Table des matières:
- Editors Introduction
- Editors Introduction
- Invited Papers
- The Discovery Science Project in Japan
- Queries Revisited
- Robot Baby 2001
- Discovering Mechanisms: A Computational Philosophy of Science Perspective
- Inventing Discovery Tools: Combining Information Visualization with Data Mining
- Complexity of Learning
- On Learning Correlated Boolean Functions Using Statistical Queries (Extended Abstract)
- A Simpler Analysis of the Multi-way Branching Decision Tree Boosting Algorithm
- Minimizing the Quadratic Training Error of a Sigmoid Neuron Is Hard
- Support Vector Machines
- Learning of Boolean Functions Using Support Vector Machines
- A Random Sampling Technique for Training Support Vector Machines
- New Learning Models
- Learning Coherent Concepts
- Learning Intermediate Concepts
- Real-Valued Multiple-Instance Learning with Queries
- Online Learning
- Loss Functions, Complexities, and the Legendre Transformation
- Non-linear Inequalities between Predictive and Kolmogorov Complexities
- Inductive Inference
- Learning by Switching Type of Information
- Learning How to Separate
- Learning Languages in a Union
- On the Comparison of Inductive Inference Criteria for Uniform Learning of Finite Classes
- Refutable Inductive Inference
- Refutable Language Learning with a Neighbor System
- Learning Recursive Functions Refutably
- Refuting Learning Revisited
- Learning Structures and Languages
- Efficient Learning of Semi-structured Data from Queries
- Extending Elementary Formal Systems
- Learning Regular Languages Using RFSA
- Inference of ?-Languages from Prefixes.