Algorithmic Learning Theory(算法学习理论/会议录)

加载中...

This book constitutes the refereed proceedings of the 15th International Conference on Algorithmic Learning Theory, ALT 2004, held in Padova, Italy in October 2004.
The 29 revised full papers presented together with 5 invited papers and 3 tutorial summaries were carefully reviewed and selected from 91 submissions. The papers are organized in topical sections on inductive inference, PAC learning and boosting, statistical supervised learning, online sequence learning, approximate optimization algorithms, logic based learning, and query and reinforcement learning.

INVITED PAPERS
　String Pattern Discovery
　Applications of Regularized Least Squares to Classification Problems .
　Probabilistic Inductive Logic Programming
　Hidden Markov Modelling Techniques for Haplotype Analysis
　Learning, Logic, and Probability: A Unified View
　REGULAR CONTRIBUTIONS
Inductive Inference
　Learning Languages from Positive Data　and Negative Counterexamples
　Inductive Inference of Term Rewriting Systems from Positive Data
　On the Data Consumption Benefits　of Accepting Increased Uncertainty
　Comparison of Query Learning and Gold-Style Learning　in Dependence of the Hypothesis Space
PAC Learning and Boosting
　Learning r-of-k Functions by Boosting
　Boosting Based on Divide and Merge
　Learning Boolean Functions in AC0 on Attribute　and Classification Noise
Statistical Supervised Learning
　Decision Trees: More Theoretical Justification　for Practical Algorithms
　Application of Classical Nonparametric Predictors　to Learning Conditionally I.I.D. Data
　Complexity of Pattern Classes and Lipschitz Property
Statistical Analysis of Unlabeled Data
　On Kernels, Margins, and Low-Dimensional Mappings
　Estimation of the Data Region Using Extreme-Value Distributions
　Maximum Entropy Principle in Non-ordered Setting
　Universal Convergence of Semimeasures　on Individual Random Sequences
Online Sequence Prediction
　A Criterion for the Existence of Predictive Complexity　for Binary Games
　Full Information Game with Gains and Losses
　Prediction with Expert Advice by Following the Perturbed Leader　for General Weights
　On the Convergence Speed of MDL Predictions　for Bernoulli Sequences
Aooroximate Optimzation Algorthms
Logic Based Learning
Query and Reinforcement Learning
TUTORIAL PAPERS
Author Index