Algorithmic Learning Theory 14th International Conference, ALT 2003, Sapporo, Japan, October 17-19, 2003, Proceedings /
| Συγγραφή απο Οργανισμό/Αρχή: | |
|---|---|
| Άλλοι συγγραφείς: | , , |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2003.
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| Έκδοση: | 1st ed. 2003. |
| Σειρά: | Lecture Notes in Artificial Intelligence ;
2842 |
| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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.
