Learning Theory [[electronic resource] ] : 17th Annual Conference on Learning Theory, COLT 2004, Banff, Canada, July 1-4, 2004, Proceedings / / edited by John Shawe-Taylor, Yoram Singer

• Pubblicazione: Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2004
• Edizione: 1st ed. 2004.
• Descrizione fisica: 1 online resource (X, 654 p.)
• Disciplina: 006.31
• Soggetto topico: Artificial intelligence; Mathematical logic; Algorithms; Computers; Artificial Intelligence; Mathematical Logic and Formal Languages; Algorithm Analysis and Problem Complexity; Computation by Abstract Devices
• Persona (resp. second.): Shawe-TaylorJohn; SingerYoram
• Note generali: Includes index.
• Nota di contenuto: 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.
• Titolo autorizzato: Learning Theory
• ISBN: 3-540-27819-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996465807203316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture Notes in Artificial Intelligence ; ; 3120