Algorithmic learning theory : 15th international conference, ALT 2004, Padova, Italy, October 2-5, 2004 : proceedings
Algorithmic learning theory is mathematics about computer programs which learn from experience. This involves considerable interaction between various mathematical disciplines including theory of computation, statistics, and c- binatorics. There is also considerable interaction with the practical, e...
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| Format: | Livre numérique |
| Sprog: | Anglais |
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Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Serier: | Lecture notes in computer science. Lecture notes in artificial intelligence
3244 |
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| Online adgang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
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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, 15th international conference, ALT 2004, Padova, Italy, October 2-5, 2004, proceedings, Shai Ben-David, John Case, Akira Maruoka (Eds.), Berlin, Springer, 2004, 1 vol. (XIV-504 p.), Lecture notes in computer science, 3-540-23356-3 • Algorithmic Learning Theory, Texte imprimé, 9783662205204 |
Indholdsfortegnelse:
- Invited Papers
- String Pattern Discovery
- Applications of Regularized Least Squares to Classification Problems
- Probabilistic Inductive Logic Programming
- Hidden Markov Modelling Techniques for Haplotype Analysis
- Learning, Logic, and Probability: A Unified View
- Regular Contributions
- Learning Languages from Positive Data and Negative Counterexamples
- Inductive Inference of Term Rewriting Systems from Positive Data
- On the Data Consumption Benefits of Accepting Increased Uncertainty
- Comparison of Query Learning and Gold-Style Learning in Dependence of the Hypothesis Space
- Learning r-of-k Functions by Boosting
- Boosting Based on Divide and Merge
- Learning Boolean Functions in AC 0 on Attribute and Classification Noise
- Decision Trees: More Theoretical Justification for Practical Algorithms
- Application of Classical Nonparametric Predictors to Learning Conditionally I.I.D. Data
- Complexity of Pattern Classes and Lipschitz Property
- On Kernels, Margins, and Low-Dimensional Mappings
- Estimation of the Data Region Using Extreme-Value Distributions
- Maximum Entropy Principle in Non-ordered Setting
- Universal Convergence of Semimeasures on Individual Random Sequences
- A Criterion for the Existence of Predictive Complexity for Binary Games
- Full Information Game with Gains and Losses
- Prediction with Expert Advice by Following the Perturbed Leader for General Weights
- On the Convergence Speed of MDL Predictions for Bernoulli Sequences
- Relative Loss Bounds and Polynomial-Time Predictions for the k-lms-net Algorithm
- On the Complexity of Working Set Selection
- Convergence of a Generalized Gradient Selection Approach for the Decomposition Method
- Newton Diagram and Stochastic Complexity in Mixture of Binomial Distributions
- Learnability of Relatively Quantified Generalized Formulas
- Learning Languages Generated by Elementary Formal Systems and Its Application to SH Languages
- New Revision Algorithms
- The Subsumption Lattice and Query Learning
- Learning of Ordered Tree Languages with Height-Bounded Variables Using Queries
- Learning Tree Languages from Positive Examples and Membership Queries
- Learning Content Sequencing in an Educational Environment According to Student Needs
- Tutorial Papers
- Statistical Learning in Digital Wireless Communications
- A BP-Based Algorithm for Performing Bayesian Inference in Large Perceptron-Type Networks
- Approximate Inference in Probabilistic Models.