Algorithmic learning theory : 13th international conference, ALT 2002, Lübeck, Germany, November 24-26, 2002 : proceedings
This volume contains the papers presented at the 13th Annual Conference on Algorithmic Learning Theory (ALT 2002), which was held in Lub eck (Germany) during November 24 26, 2002. The main objective of the conference was to p- vide an interdisciplinary forum discussing the theoretical foundations of...
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| Edition sous un autre format: | • Algorithmic learning theory, 13th international conference, ALT 2002, Lübeck, Germany, November 24-26, 2002, proceedings, Nicolò Cesa-Bianchi, Masayuki Numao, Rüdiger Reischuk (eds.), Berlin, Springer, 2002, 1 vol. (XI-413 p.), Lecture notes in computer science, 3-540-00170-0 • Algorithmic Learning Theory, Texte imprimé, 9783662184738 |
Obsah:
- Editors Introduction
- Editors Introduction
- Invited Papers
- Mathematics Based on Learning
- Data Mining with Graphical Models
- On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum
- In Search of the Horowitz Factor: Interim Report on a Musical Discovery Project
- Learning Structure from Sequences, with Applications in a Digital Library
- Regular Contributions
- On Learning Monotone Boolean Functions under the Uniform Distribution
- On Learning Embedded Midbit Functions
- Maximizing Agreements and CoAgnostic Learning
- Optimally-Smooth Adaptive Boosting and Application to Agnostic Learning
- Large Margin Classification for Moving Targets
- On the Smallest Possible Dimension and the Largest Possible Margin of Linear Arrangements Representing Given Concept Classes Uniform Distribution
- A General Dimension for Approximately Learning Boolean Functions
- The Complexity of Learning Concept Classes with Polynomial General Dimension
- On the Absence of Predictive Complexity for Some Games
- Consistency Queries in Information Extraction
- Ordered Term Tree Languages which Are Polynomial Time Inductively Inferable from Positive Data
- Reflective Inductive Inference of Recursive Functions
- Classes with Easily Learnable Subclasses
- On the Learnability of Vector Spaces
- Learning, Logic, and Topology in a Common Framework
- A Pathology of Bottom-Up Hill-Climbing in Inductive Rule Learning
- Minimised Residue Hypotheses in Relevant Logic
- Compactness and Learning of Classes of Unions of Erasing Regular Pattern Languages
- A Negative Result on Inductive Inference of Extended Pattern Languages
- RBF Neural Networks and Descartes Rule of Signs
- Asymptotic Optimality of Transductive Confidence Machine
- An Efficient PAC Algorithm forReconstructing a Mixture of Lines
- Constraint Classification: A New Approach to Multiclass Classification
- How to Achieve Minimax Expected Kullback-Leibler Distance from an Unknown Finite Distribution
- Classification with Intersecting Rules
- Feedforward Neural Networks in Reinforcement Learning Applied to High-Dimensional Motor Control.