Learning Theory 17th Annual Conference on Learning Theory, COLT 2004, Banff, Canada, July 1-4, 2004. Proceedings /

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Shawe-Taylor, John (Επιμελητής έκδοσης), Singer, Yoram (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2004.
Σειρά:Lecture Notes in Computer Science, 3120
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • 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 for Intersection-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.