学习理论： COLT 2006/会议录Learning theory
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作者： Hans Ulrich Simon，Gábor Lugosi 著
出 版 社： 湖北辞书出版社
出版时间： 2006-12-1字数：版次： 1页数： 656印刷时间： 2006/12/01开本：印次：纸张： 胶版纸I S B N ： 9783540352945包装： 平装编辑推荐
The LNAI series reports state-of-the-art results in artificial intelligence re-search, development, and education, at a high level and in both printed and electronic form. Enjoying tight cooperation with the R&D community, with numerous individuals, as well as with prestigious organizations and societies, LNAI has grown into the most comprehensive artificial intelligence research forum available.
The scope of LNAI spans the whole range of artificial intelligence and intelli- gent information processing including interdisciplinary topics in a variety of application fields. The type of material published traditionally includes.
proceedings (published in time for the respective conference)；
post-proceedings (consisting of thoroughly revised final full papers)；
research monographs (which may be based on PhD work).
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
