Algorithmic Learning Theory 算法学习理论

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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 Phi) work).

Editors' Introduction
Invited Papers
The Discovery Science Project in Japan
Queries Revisited
Robot Baby 2001
Discovering Mechanisms: A Computational Philosophy of Science
Perspective
Inventing Discovery Tools: Combining Information Visualization
with Data Mining
Complexity of Learning
On Learning Correlated Boolean Functions Using Statistical Queries
A Simpler Analysis of the Multi-way Branching Decision Tree Boosting Algorithm
Minimizing the Quadratic Training Error of a Sigmoid Neuron Is Hard
Support Vector Machines
Learning of Boolean Functions Using Support Vector Machines
A Random Sampling Technique for Training Support
Vector Machines (For Primal-Form Maximal-Margin Classifiers)
New Learning Models
Learning Coherent Concepts
Learning Intermediate Concepts
Real-Valued Multiple-Instance Learning with Queries
Online Learning
Loss Functions, Complexities, and the Legendre Transformation
Non-linear Inequalities between Predictive and Kolmogorov Complexities
Inductive Inference
Learning by Switching Type of Information
Learning How to Separate
Learning Languages in a Union
On the Comparison of Inductive Inference Criteria for Uniform Learning　of Finite Classes
Refutable Inductive Inference
Refutable Language Learning with a Neighbor System
Learning Recursive Functions Refutably
Refuting Learning Revisited
Learning Structures and Languages
Efficient Learning of Semi-structured Data from Queries
……
Author Index