算法学习理论： ALT 2006/会议录 lgorithmic learning theory
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作者： José L. Balcázar 著
出 版 社： 湖南文艺出版社
出版时间： 2006-12-1字数：版次： 1页数： 392印刷时间： 2006/12/01开本：印次：纸张： 胶版纸I S B N ： 9783540466499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 17th International Conference on Algorithmic Learning Theory, ALT 2006, held in Barcelona, Spain in October 2006, colocated with the 9th International Conference on Discovery Science, DS 2006.
The 24 revised full papers presented together with the abstracts of 5 invited papers were carefully reviewed and selected from 53 submissions. The papers are dedicated to the theoretical foundations of machine learning; they address topics such as query models, on-line learning, inductive inference, algorithmic forecasting, boosting, support vector machines, kernel methods, reinforcement learning, and statistical learning models.
目录
Editors' Introduction
Invited Contributions
Solving Semi-infinite Linear Programs Using Boosting-Like Methods
e-Science and the Semantic Web: A Symbiotic Relationship
Spectral Norm in Learning Theory: Some Selected Topics
Data-Driven Discovery Using Probabilistic Hidden Variable Models
Reinforcement Learning and Apprenticeship Learning for Robotic Control
Regular Contributions
Learning Unions of co(l)-Dimensional Rectangles
On Exact Learning Halfspaces with Random Consistent Hypothesis Oracle
Active Learning in the Non-realizable Case
How Many Query Superpositions Are Needed to Learn?
Teaching Memoryless Randomized Learners Without Feedback
The Complexity of Learning SUBSEQ(A)
Mind Change Complexity of Inferring Unbounded Unions of Pattern Languages from Positive Data
Learning and Extending Sublanguages
Iterative Learning from Positive Data and Negative Counterexamples
Towards a Better Understanding of Incremental Learning
On Exact Learning from Random Walk
Risk-Sensitive Online Learning
Leading Strategies in Competitive On-Line Prediction
Hannah Consistency in On-Line Learning in Case of Unbounded Losses Under Partial Monitoring
General Discounting Versus Average Reward
The Missing Consistency Theorem for Bayesian Learning: Stochastic Model Selection
Is There an Elegant Universal Theory of Prediction?
Learning Linearly Separable Languages
Smooth Boosting 0-sing an Inf'ormation-l~asecf Cri'teri'on
……
Author Index
