学习理论： COLT 2006/会议录Learning theory

作者: Gábor Lugosi 著 , Hans Ulrich Simon

出版社: 崇文书局（原湖北辞书出版社）

出版时间: 2006-12

版次: 1

ISBN: 9783540352945

定价: 858.80

装帧: 平装

开本: 其他

纸张: 胶版纸

页数: 656页

分类: 外文古旧书>英文书>工程技术

This book constitutes the refereed proceedings of the 19th Annual Conference on Learning Theory, COLT 2006, held in Pittsburgh, Pennsylvania, USA in June 2006. The 43 revised full papers presented together with 2 articles on open problems and 3 invited lectures were carefully reviewed and selected from a total of 102 submissions. The papers cover a wide range of topics including clustering, un- and semisupervised learning, statistical learning theory, regularized learning and kernel methods, query learning and teaching, inductive inference, learning algorithms and limitations on learning, online aggregation, online prediction and reinforcement learning. Invited Presentations

  Random Multivariate Search Trees

  On Learning and Logic

  Predictions as Statements and Decisions

Clustering, Un-, and Semisupervised Learning

  A Sober Look at Clustering Stability

  PAC Learning Axis-Aligned Mixtures of Gaussians with No Separation Assumption

  Stable Transductive Learning

  Uniform Convergence of Adaptive Graph-Based Regularization

Statistical Learning Theory

  The Rademacher Complexity of Linear Transformation Classes

  Function Classes That Approximate the Bayes Risk

  Functional Classification with Margin Conditions

  Significance and Recovery of Block Structures in Binary Matrices with Noise

Regularized Learning and Kernel Methods

  Maximum Entropy Distribution Estimation with Generalized Regularization

  Unifying Divergence Minimization and Statistical Inference Via Convex Duality

  Mercer's Theorem, Feature Maps, and Smoothing

  Learning Bounds for Support Vector Machines with Learned Kernels

Query Learning and Teaching

  On Optimal Learning Algorithms for Multiplicity Automata

  Exact Learning Composed Classes with a Small Number of Mistakes

  DNF Are Teachable in the Average Case

  Teaching Randomized Learners

Inductive Inference

  Memory-Limited U-Shaped Learning

  On Learning Languages from Positive Data and a Limited Number of Short Counterexamples

  Learning Rational Stochastic Languages

  Parent Assignment Is Hard for the MDL, AIC, and NML Costs

Learning Algorithms and Limitations on Learning

Online Aggregation

Online Prediction and Reinforcement Learning Ⅰ

Online Prediction and Reinforcement Learning Ⅱ

Online Prediction and Reinforcement Learning Ⅲ

Other Approaches

Open Problems

Author Index
内容简介:
This book constitutes the refereed proceedings of the 19th Annual Conference on Learning Theory, COLT 2006, held in Pittsburgh, Pennsylvania, USA in June 2006. The 43 revised full papers presented together with 2 articles on open problems and 3 invited lectures were carefully reviewed and selected from a total of 102 submissions. The papers cover a wide range of topics including clustering, un- and semisupervised learning, statistical learning theory, regularized learning and kernel methods, query learning and teaching, inductive inference, learning algorithms and limitations on learning, online aggregation, online prediction and reinforcement learning.
目录:
Invited Presentations

  Random Multivariate Search Trees

  On Learning and Logic

  Predictions as Statements and Decisions

Clustering, Un-, and Semisupervised Learning

  A Sober Look at Clustering Stability

  PAC Learning Axis-Aligned Mixtures of Gaussians with No Separation Assumption

  Stable Transductive Learning

  Uniform Convergence of Adaptive Graph-Based Regularization

Statistical Learning Theory

  The Rademacher Complexity of Linear Transformation Classes

  Function Classes That Approximate the Bayes Risk

  Functional Classification with Margin Conditions

  Significance and Recovery of Block Structures in Binary Matrices with Noise

Regularized Learning and Kernel Methods

  Maximum Entropy Distribution Estimation with Generalized Regularization

  Unifying Divergence Minimization and Statistical Inference Via Convex Duality

  Mercer's Theorem, Feature Maps, and Smoothing

  Learning Bounds for Support Vector Machines with Learned Kernels

Query Learning and Teaching

  On Optimal Learning Algorithms for Multiplicity Automata

  Exact Learning Composed Classes with a Small Number of Mistakes

  DNF Are Teachable in the Average Case

  Teaching Randomized Learners

Inductive Inference

  Memory-Limited U-Shaped Learning

  On Learning Languages from Positive Data and a Limited Number of Short Counterexamples

  Learning Rational Stochastic Languages

  Parent Assignment Is Hard for the MDL, AIC, and NML Costs

Learning Algorithms and Limitations on Learning

Online Aggregation

Online Prediction and Reinforcement Learning Ⅰ

Online Prediction and Reinforcement Learning Ⅱ

Online Prediction and Reinforcement Learning Ⅲ

Other Approaches

Open Problems

Author Index

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学习理论： COLT 2006/会议录Learning theory

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