计算学习理论/会议录 Computational learning theory
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作者： Jyrki Kivinen著
出 版 社： 湖南文艺出版社
出版时间： 2002-12-1字数：版次： 1页数： 395印刷时间： 2006/12/01开本：印次：纸张： 胶版纸I S B N ： 9783540438366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 15th Annual Conference on Computational Learning Theory, COLT 2002, held in Sydney, Australia, in July 2002.The 26 revised full papers presented were carefully reviewed and selected from 55 submissions. The papers are organized in topical sections on statistical learning theory, online learning, inductive inference, PAC learning, boosting, and other learning paradigms.
目录
Statistical Learning Theory
Agnostic Learning Nonconvex Function Classes
Entropy, Combinatorial Dimensions and Random Averages
Geometric Parameters of Kernel Machines
Localized Rademacher Complexities
Some Local Measures of Complexity of Convex Hulls and Generalization Bounds
Online Learning
Path Kernels and Multiplicative Updates
Predictive Complexity and Information
Mixability and the Existence of Weak Complexities
A Second-Order Perceptron Algorithm
Tracking Linear-Threshold Concepts with Winnow
Inductive Inference
Learning Tree Languages from Text
Polynomial Time Inductive Inference of Ordered Tree Patterns with Internal Structured Variables from Positive Data
Inferring Deterministic Linear Languages
Merging Uniform Inductive Learners
The Speed Prior: A New Simplicity Measure
PAC Learning
New Lower Bounds for Statistical Query Learning
Exploring Learnability between Exact and PAC
PAC Bounds for Multi-armed Bandit and Markov Decision Processes
Bounds for the Minimum Disagreement Problem with Applications to Learning Theory
On the Proper Learning of Axis Parallel Concepts
Boosting
A Consistent Strategy for Boosting Algorithms
The Consistency of Greedy Algorithms for Classification
Maximizing the Margin with Boosting
Other Learning Paradigms
Performance Guarantees for Hierarchical Clustering
Self-Optimizing and Pareto-Optimal Policies in General Environments Based on Bayes-Mixtures
Prediction and Dimension
Invited Talk
Author Index
