Algorithmic learning theory : 10th International Conference, ALT 99, Tokyo, Japan, December 6-8, 1999 : proceedings
সংরক্ষণ করুন:
| প্রধান লেখক: | |
|---|---|
| সংস্থা লেখক: | |
| অন্যান্য লেখক: | |
| বিন্যাস: | Livre numérique |
| ভাষা: | Anglais |
| প্রকাশিত: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| মালা: | Lecture notes in computer science. Lecture notes in artificial intelligence
1720 |
| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| টীকা: |
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, 10th International Conference, ALT'99, Tokyo, Japan, December 1999, proceedings, Osamu Watanabe, Takashi Yokomori (eds.), 1999, New York, Springer, 1 vol. (XI-363 p.), Lecture notes in computer science, 3-540-66748-2 • Algorithmic Learning Theory, Texte imprimé, 9783662165096 |
সূচিপত্রের সারণি:
- 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.