Algorithmic learning theory : 14th international conference, ALT 2003, Sapporo, Japan, October 17-19, 2003 : proceedings
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| 其他作者: | , , |
| 格式: | Livre numérique |
| 語言: | Anglais |
| 出版: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| 叢編: | Lecture notes in computer science. Lecture notes in artificial intelligence
2842 |
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| 在線閱讀: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
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| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Algorithmic learning theory, 14th international conference, ALT 2003, Sapporo, Japan, October 17-19, 2003, proceedings, ed. by Ricard Gavalda, Klaus P. Jantke, Eiji Takimoto, Berlin, Springer, 2003, 1 vol. (XI-312 p.), Lecture notes in computer science, 3-540-20291-9 • Algorithmic Learning Theory, Texte imprimé, 9783662163023 |
書本目錄:
- Invited Papers
- Abduction and the Dualization Problem
- Signal Extraction and Knowledge Discovery Based on Statistical Modeling
- Association Computation for Information Access
- Efficient Data Representations That Preserve Information
- Can Learning in the Limit Be Done Efficiently?
- Inductive Inference
- Intrinsic Complexity of Uniform Learning
- On Ordinal VC-Dimension and Some Notions of Complexity
- Learning of Erasing Primitive Formal Systems from Positive Examples
- Changing the Inference Type Keeping the Hypothesis Space
- Learning and Information Extraction
- Robust Inference of Relevant Attributes
- Efficient Learning of Ordered and Unordered Tree Patterns with Contractible Variables
- Learning with Queries
- On the Learnability of Erasing Pattern Languages in the Query Model
- Learning of Finite Unions of Tree Patterns with Repeated Internal Structured Variables from Queries
- Learning with Non-linear Optimization
- Kernel Trick Embedded Gaussian Mixture Model
- Efficiently Learning the Metric with Side-Information
- Learning Continuous Latent Variable Models with Bregman Divergences
- A Stochastic Gradient Descent Algorithm for Structural Risk Minimisation
- Learning from Random Examples
- On the Complexity of Training a Single Perceptron with Programmable Synaptic Delays
- Learning a Subclass of Regular Patterns in Polynomial Time
- Identification with Probability One of Stochastic Deterministic Linear Languages
- Online Prediction
- Criterion of Calibration for Transductive Confidence Machine with Limited Feedback
- Well-Calibrated Predictions from Online Compression Models
- Transductive Confidence Machine Is Universal
- On the Existence and Convergence of Computable Universal Priors.