Communications of the ACM
Pattern languages are not learnable
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Learning pattern languages from a single initial example and from queries
COLT '88 Proceedings of the first annual workshop on Computational learning theory
Learning regular languages from counterexamples
COLT '88 Proceedings of the first annual workshop on Computational learning theory
A polynomial-time algorithm for learning k-variable pattern languages from examples
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Identification of unions of languages drawn from an identifiable class
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Prediction-preserving reducibility
Journal of Computer and System Sciences - 3rd Annual Conference on Structure in Complexity Theory, June 14–17, 1988
Learning string patterns and tree patterns from examples
Proceedings of the seventh international conference (1990) on Machine learning
Polynomial-time inference of arbitrary pattern languages
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Redundant noisy attributes, attribute errors, and linear-threshold learning using winnow
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
The weighted majority algorithm
Information and Computation
A machine discovery from amino acid sequences by decision trees over regular patterns
Selected papers of international conference on Fifth generation computer systems 92
A generalization of the least general generalization
Machine intelligence 13
Elementary formal systems, intrinsic complexity, and procrastination
Information and Computation
Learnability of a subclass of extended pattern languages
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Exact learning of tree patterns from queries and counterexamples
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning one-variable pattern languages in linear average time
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Exact learning of unordered tree patterns from queries
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Ordinal mind change complexity of language identification
Theoretical Computer Science
Agnostic learning of geometric patterns
Journal of Computer and System Sciences
Annals of Mathematics and Artificial Intelligence
Machine Learning
Machine Learning
Polynomial Time Inference of Extended Regular Pattern Languages
Proceedings of RIMS Symposium on Software Science and Engineering
Extracting Best Consensus Motifs from Positive and Negative Examples
STACS '96 Proceedings of the 13th Annual Symposium on Theoretical Aspects of Computer Science
Polynomial Time Inference of General Pattern Languages
STACS '84 Proceedings of the Symposium of Theoretical Aspects of Computer Science
Learning Pattern Languages Using Queries
EuroCOLT '97 Proceedings of the Third European Conference on Computational Learning Theory
Case-Based Representation and Learning of Pattern Languages
ALT '93 Proceedings of the 4th International Workshop on Algorithmic Learning Theory
Learning Unions of Tree Patterns Using Queries
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
ALT '97 Proceedings of the 8th International Conference on Algorithmic Learning Theory
More About Learning Elementary Formal Systems
Proceedings of the Second International Workshop on Nonmonotonic and Inductive Logic
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
POPL '84 Proceedings of the 11th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Journal of Computer and System Sciences
Learning a subclass of regular patterns in polynomial time
Theoretical Computer Science - Algorithmic learning theory
DLT'03 Proceedings of the 7th international conference on Developments in language theory
Learning from positive data based on the MINL strategy with refinement operators
JSAI-isAI'09 Proceedings of the 2009 international conference on New frontiers in artificial intelligence
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We present efficient on-line algorithms for learning unions of a constant number of tree patterns, unions of a constant number of one-variable pattern languages, and unions of a constant number of pattern languages with fixed length substitutions. By fixed length substitutions we mean that each occurrence of variable xi must be substituted by terminal strings of fixed length l(xi). We prove that if arbitrary unions of pattern languages with fixed length substitutions can be learned efficiently then DNFs are efficiently learnable in the mistake bound model. Since we use a reduction to Winnow, our algorithms are robust against attribute noise. Furthermore, they can be modified to handle concept drift. Also, our approach is quite general and we give results to learn a class that generalizes pattern languages.