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Learning Theory

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Inhalt Economics and Game Theory.- Towards a Characterization of Polynomial Preference Elicitation with Value Queries in Combinato... Weiterlesen
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Beschreibung

Inhalt

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.

Produktinformationen

Titel: Learning Theory
Untertitel: 17th Annual Conference on Learning Theory, COLT 2004, Banff, Canada, July 1-4, 2004, Proceedings
Editor:
EAN: 9783540278191
Digitaler Kopierschutz: Wasserzeichen
Format: E-Book (pdf)
Hersteller: Springer Berlin Heidelberg
Genre: IT & Internet
Anzahl Seiten: 654
Veröffentlichung: 11.06.2004
Dateigrösse: 6.8 MB

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