Learning theory and Kernel machines : 16th annual Conference on Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003 : proceedings
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| Médium: | Livre numérique |
| Jazyk: | Anglais |
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Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Edice: | Lecture notes in computer science. Lecture notes in artificial intelligence
2777 |
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| Edition sous un autre format: | • Learning theory and Kernel machines, 16th annual Conference on Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003, proceedings, Bernhard Schölkopf, Manfred K. Warmuth (eds.), Berlin, Springer, 2003, 1 vol. (XIV-746 p.), Lecture notes in computer science, 3-540-40720-0 • Learning Theory and Kernel Machines, Texte imprimé, 9783662164792 |
Obsah:
- Target Area: Computational Game Theory
- Tutorial: Learning Topics in Game-Theoretic Decision Making
- A General Class of No-Regret Learning Algorithms and Game-Theoretic Equilibria
- Preference Elicitation and Query Learning
- Efficient Algorithms for Online Decision Problems
- Positive Definite Rational Kernels
- Bhattacharyya and Expected Likelihood Kernels
- Maximal Margin Classification for Metric Spaces
- Maximum Margin Algorithms with Boolean Kernels
- Knowledge-Based Nonlinear Kernel Classifiers
- Fast Kernels for Inexact String Matching
- On Graph Kernels: Hardness Results and Efficient Alternatives
- Kernels and Regularization on Graphs
- Data-Dependent Bounds for Multi-category Classification Based on Convex Losses
- Poster Session 1
- Comparing Clusterings by the Variation of Information
- Multiplicative Updates for Large Margin Classifiers
- Simplified PAC-Bayesian Margin Bounds
- Sparse Kernel Partial Least Squares Regression
- Sparse Probability Regression by Label Partitioning
- Learning with Rigorous Support Vector Machines
- Robust Regression by Boosting the Median
- Boosting with Diverse Base Classifiers
- Reducing Kernel Matrix Diagonal Dominance Using Semi-definite Programming
- Optimal Rates of Aggregation
- Distance-Based Classification with Lipschitz Functions
- Random Subclass Bounds
- PAC-MDL Bounds
- Universal Well-Calibrated Algorithm for On-Line Classification
- Learning Probabilistic Linear-Threshold Classifiers via Selective Sampling
- Learning Algorithms for Enclosing Points in Bregmanian Spheres
- Internal Regret in On-Line Portfolio Selection
- Lower Bounds on the Sample Complexity of Exploration in the Multi-armed Bandit Problem
- Smooth ?-Insensitive Regression by Loss Symmetrization
- On Finding Large Conjunctive Clusters
- LearningArithmetic Circuits via Partial Derivatives
- Poster Session 2
- Using a Linear Fit to Determine Monotonicity Directions
- Generalization Bounds for Voting Classifiers Based on Sparsity and Clustering
- Sequence Prediction Based on Monotone Complexity
- How Many Strings Are Easy to Predict?
- Polynomial Certificates for Propositional Classes
- On-Line Learning with Imperfect Monitoring
- Exploiting Task Relatedness for Multiple Task Learning
- Approximate Equivalence of Markov Decision Processes
- An Information Theoretic Tradeoff between Complexity and Accuracy
- Learning Random Log-Depth Decision Trees under the Uniform Distribution
- Projective DNF Formulae and Their Revision
- Learning with Equivalence Constraints and the Relation to Multiclass Learning
- Target Area: Natural Language Processing
- Tutorial: Machine Learning Methods in Natural Language Processing
- Learning from Uncertain Data
- Learning and Parsing Stochastic Unification-Based Grammars
- Generality s Price
- On Learning to Coordinate
- Learning All Subfunctions of a Function
- When Is Small Beautiful?
- Learning a Function of r Relevant Variables
- Subspace Detection: A Robust Statistics Formulation
- How Fast Is k-Means?
- Universal Coding of Zipf Distributions
- An Open Problem Regarding the Convergence of Universal A Priori Probability
- Entropy Bounds for Restricted Convex Hulls
- Compressing to VC Dimension Many Points.