This book constitutes the refereed proceedings of the 16th International Conference on Algorithmic Learning Theory, ALT 2005, held in Singapore in October 2005. The 30 revised full papers presented together with 5 invited papers and an introduction by the editors were carefully reviewed and selected from 98 submissions. The papers are organized in topical sections on kernel-based learning, bayesian and statistical models, PAC-learning, query-learning, inductive inference, language learning, learning and logic, learning from expert advice, online learning, defensive forecasting, and teaching.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)Editors' Introduction
Invited Papers
Invention and Artificial Intelligence
The Arrowsmith Project: 2005 Status Report
The Robot Scientist Project
Algorithms and Software for Collaborative Discovery from Autonomous, Semantically Heterogeneous, Distributed Information Sources
Training Support Vector Machines via SMO-Type Decomposition Methods
Kernel-Based Learning
Regular Contributions
Measuring Statistical Dependence with Hilbert-Schmidt Norms
An Analysis of the Anti-learning Phenomenon for the Class Symmetric Polyhedron
Bayesian and Statistical Models
Learning Causal Structures Based on Markov Equivalence Class
Stochastic Complexity for Mixture of Exponential Families in Variational Bayes
ACME: An Associative Classifier Based on Maximum Entropy Principle
PAC-Learning
Constructing Multiclass Learners from Binary Learners:A Simple Black-Box Analysis of the Generalization Errors
On Computability of Pattern Recognition Problems
PAC-Learnability of Probabilistic Deterministic Finite State Automata in Terms of Variation Distance
Learnability of Probabilistic Automata via Oracles
Query-Learning
Learning Attribute-Efficiently with Corrupt Oracles
Learning DNF by Statistical and Proper Distance Queries Under the Uniform Distribution
Learning of Elementary Formal Systems with Two Clauses Using Queries
Gold-Style and Query Learning Under Various Constraints on the Target Class
Inductive Inference
Non U-Shaped Vacillatory and Team Learning
Learning Multiple Languages in Groups
Language Learning
Learning and Logic
Learning from Expert Advice
Online Learning
Defensive Forecasting
Teaching
Author Index