Learning theory : 17th Annual Conference on Learning Theory, COLT 2004, Banff, Canada, July 1-4, 2004 : proceedings
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| Otros Autores: | , |
| Formato: | Livre numérique |
| Lenguaje: | Anglais |
| Publicado: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Colección: | Lecture notes in computer science. Lecture notes in artificial intelligence
3120 |
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| Acceso en línea: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Learning theory, 17th Annual Conference on Learning Theory, COLT 2004, Banff, Canada, July 1-4, 2004, proceedings, John Shawe-Taylor, Yoram Singer (eds.), Berlin, Springer, 2004, 1 vol. (X-645 p.), Lecture notes in computer science, 3-540-22282-0 • Learning Theory, Texte imprimé, 9783662204115 |
Tabla de Contenidos:
- Economics and Game Theory
- Towards a Characterization of Polynomial Preference Elicitation with Value Queries in Combinatorial Auctions
- Graphical Economics
- Deterministic Calibration and Nash Equilibrium
- Reinforcement Learning for Average Reward Zero-Sum Games
- OnLine Learning
- Polynomial Time Prediction Strategy with Almost Optimal Mistake Probability
- Minimizing Regret with Label Efficient Prediction
- Regret Bounds for Hierarchical Classification with Linear-Threshold Functions
- Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary
- Inductive Inference
- Learning Classes of Probabilistic Automata
- On the Learnability of E-pattern Languages over Small Alphabets
- Replacing Limit Learners with Equally Powerful One-Shot Query Learners
- Probabilistic Models
- Concentration Bounds for Unigrams Language Model
- Inferring Mixtures of Markov Chains
- Boolean Function Learning
- PExact = Exact Learning
- Learning a Hidden Graph Using O(log n) Queries Per Edge
- Toward Attribute Efficient Learning of Decision Lists and Parities
- Empirical Processes
- Learning Over Compact Metric Spaces
- A Function Representation for Learning in Banach Spaces
- Local Complexities for Empirical Risk Minimization
- Model Selection by Bootstrap Penalization for Classification
- MDL
- Convergence of Discrete MDL for Sequential Prediction
- On the Convergence of MDL Density Estimation
- Suboptimal Behavior of Bayes and MDL in Classification Under Misspecification
- Generalisation I
- Learning Intersections of Halfspaces with a Margin
- A General Convergence Theorem for the Decomposition Method
- Generalisation II
- Oracle Bounds and Exact Algorithm for Dyadic Classification Trees
- An Improved VC Dimension Bound for Sparse Polynomials
- A New PAC Bound forIntersection-Closed Concept Classes
- Clustering and Distributed Learning
- A Framework for Statistical Clustering with a Constant Time Approximation Algorithms for K-Median Clustering
- Data Dependent Risk Bounds for Hierarchical Mixture of Experts Classifiers
- Consistency in Models for Communication Constrained Distributed Learning
- On the Convergence of Spectral Clustering on Random Samples: The Normalized Case
- Boosting
- Performance Guarantees for Regularized Maximum Entropy Density Estimation
- Learning Monotonic Linear Functions
- Boosting Based on a Smooth Margin
- Kernels and Probabilities
- Bayesian Networks and Inner Product Spaces
- An Inequality for Nearly Log-Concave Distributions with Applications to Learning
- Bayes and Tukey Meet at the Center Point
- Sparseness Versus Estimating Conditional Probabilities: Some Asymptotic Results
- Kernels and Kernel Matrices
- A Statistical Mechanics Analysis of Gram Matrix Eigenvalue Spectra
- Statistical Properties of Kernel Principal Component Analysis
- Kernelizing Sorting, Permutation, and Alignment for Minimum Volume PCA
- Regularization and Semi-supervised Learning on Large Graphs
- Open Problems
- Perceptron-Like Performance for Intersections of Halfspaces
- The Optimal PAC Algorithm
- The Budgeted Multi-armed Bandit Problem.