Learning theory : 17th Annual Conference on Learning Theory, COLT 2004, Banff, Canada, July 14, 2004 : proceedings
 Responsibility
 John ShaweTaylor, Yoram Singer (eds.).
 Digital
 text file; PDF
 Publication
 Berlin : Springer, 2004.
 Physical description
 1 online resource (x, 645 pages) : illustrations
 Series
 Lecture notes in computer science ; 3120.
 Lecture notes in computer science. Lecture notes in artificial intelligence.
Online
More options
Description
Creators/Contributors
Contents/Summary
 Bibliography
 Includes bibliographical references and index.
 Contents

 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 ZeroSum Games
 OnLine Learning
 Polynomial Time Prediction Strategy with Almost Optimal Mistake Probability
 Minimizing Regret with Label Efficient Prediction
 Regret Bounds for Hierarchical Classification with LinearThreshold Functions
 Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary
 Inductive Inference
 Learning Classes of Probabilistic Automata
 On the Learnability of Epattern Languages over Small Alphabets
 Replacing Limit Learners with Equally Powerful OneShot 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 IntersectionClosed Concept Classes
 Clustering and Distributed Learning
 A Framework for Statistical Clustering with a Constant Time Approximation Algorithms for KMedian 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 LogConcave 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 Semisupervised Learning on Large Graphs
 Open Problems
 PerceptronLike Performance for Intersections of Halfspaces
 The Optimal PAC Algorithm
 The Budgeted Multiarmed Bandit Problem.
 Summary
 This book constitutes the refereed proceedings of the 17th Annual Conference on Learning Theory, COLT 2004, held in Banff, Canada in July 2004. The 46 revised full papers presented were carefully reviewed and selected from a total of 113 submissions. The papers are organized in topical sections on economics and game theory, online learning, inductive inference, probabilistic models, Boolean function learning, empirical processes, MDL, generalisation, clustering and distributed learning, boosting, kernels and probabilities, kernels and kernel matrices, and open problems.
Subjects
 Subjects
 Computational learning theory > Congresses.
 Artificial intelligence > Congresses.
 Théorie de l'apprentissage informatique > Congrès.
 Intelligence artificielle > Congrès.
 COMPUTERS > Enterprise Applications > Business Intelligence Tools.
 COMPUTERS > Intelligence (AI) & Semantics.
 Artificial intelligence
 Computational learning theory
 Genre
 proceedings (reports)
 Conference papers and proceedings
 Conference papers and proceedings.
 Actes de congrès.
Bibliographic information
 Publication date
 2004
 Title variation
 COLT 2004
 Series
 Lecture notes in computer science ; 3120
 Lecture notes in computer science. Lecture notes in artificial intelligence
 ISBN
 3540278192 electronic book
 9783540278191 electronic book
 3540222820
 9783540222828
 DOI
 10.1007/b98522