Algorithmic Learning Theory: 9th International Conference, ALT '98, Otzenhausen, Germany, October 8-10, 1998, Proceedings

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Michael M. Richter
Springer Science & Business Media, 1998 - Algorithmes - Congrès - 438 pages
This volume contains all the papers presented at the Ninth International Con- rence on Algorithmic Learning Theory (ALT’98), held at the European education centre Europ ̈aisches Bildungszentrum (ebz) Otzenhausen, Germany, October 8{ 10, 1998. The Conference was sponsored by the Japanese Society for Arti cial Intelligence (JSAI) and the University of Kaiserslautern. Thirty-four papers on all aspects of algorithmic learning theory and related areas were submitted, all electronically. Twenty-six papers were accepted by the program committee based on originality, quality, and relevance to the theory of machine learning. Additionally, three invited talks presented by Akira Maruoka of Tohoku University, Arun Sharma of the University of New South Wales, and Stefan Wrobel from GMD, respectively, were featured at the conference. We would like to express our sincere gratitude to our invited speakers for sharing with us their insights on new and exciting developments in their areas of research. This conference is the ninth in a series of annual meetings established in 1990. The ALT series focuses on all areas related to algorithmic learning theory including (but not limited to): the theory of machine learning, the design and analysis of learning algorithms, computational logic of/for machine discovery, inductive inference of recursive functions and recursively enumerable languages, learning via queries, learning by arti cial and biological neural networks, pattern recognition, learning by analogy, statistical learning, Bayesian/MDL estimation, inductive logic programming, robotics, application of learning to databases, and gene analyses.
 

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Contents

Editors Introduction
1
Scalability Issues in Inductive Logic Programming
11
Learning to Win ProcessControl Games Watching GameMasters
31
Closedness Properties in EXIdentification of Recursive Functions
46
Lower Bounds for the Complexity of Learning HalfSpaces with Membership Queries
61
Cryptographic Limitations on Parallelizing Membership and Equivalence Queries with Applications to Random SelfReductions
72
Learning Unary Output TwoTape Automata from Multiplicity and Equivalence Queries
87
Computational Aspects of Parallel AttributeEfficient Learning
103
Characteristic Sets for Unions of Regular Pattern Languages and Compactness
220
Finding a OneVariable Pattern from Incomplete Data
234
A Fast Algorithm for Discovering Optimal String Patterns in Large Text Databases
247
A Comparison of Identification Criteria for Inductive Inference of Recursive RealValued Functions
262
Predictive Learning Models for Concept Drift
276
Learning with Refutation
291
Comparing the Power of Probabilistic Learning and Oracle Identification Under Monotonicity Constraints
306
Learning Algebraic Structures from Text Using Semantical Knowledge
321

PAC Learning from Positive Statistical Queries
112
Structured WeightBased Prediction Algorithms
127
Learning from Entailment of Logic Programs with Local Variables
143
Logical Aspects of Several BottomUp Fittings
158
Learnability of Translations from Positive Examples
169
Analysis of CaseBased Representability of Boolean Functions by Monotone Theory
179
Learning Languages from Positive Data
191
Synthesizing Learners Tolerating Computable Noisy Data
205
A System for Learning Relations
336
On the Sample Complexity for Neural Trees
375
Learning Subclasses of Monotone DNF on the Uniform Distribution
385
Using Attribute Grammars for Description of Inductive Inference Search Space
400
Towards the Validation of Inductive Learning Systems
409
Consistent Polynomial Identification in the Limit
424
Author Index
439
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