Communications of the ACM
Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
Identification of unions of languages drawn from an identifiable class
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Machine learning: a theoretical approach
Machine learning: a theoretical approach
A noise model on learning sets of strings
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A machine discovery from amino acid sequences by decision trees over regular patterns
Selected papers of international conference on Fifth generation computer systems 92
Decision problems for patterns
Journal of Computer and System Sciences
Polynomial-time learning of elementary formal systems
New Generation Computing
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Polynomial Time Inference of Extended Regular Pattern Languages
Proceedings of RIMS Symposium on Software Science and Engineering
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
A Guided Tour Across the Boundaries of Learning Recursive Languages
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
Inductive Inference of Unbounded Unions of Pattern Languages from Positive Data
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
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The paper introduces the notion of decision lists over regular patterns. This formalism provides a strict extension of regular erasing pattern languages and of containment decision lists.Formal properties of the resulting language class, a subclass of the regular languages, are investigated. In particular, we show that decision lists over regular patterns have exactly the same expressive power as decision trees over regular patterns.Moreover, we study the learnability of the resulting language class within different formal settings including Gold's model of learning in the limit as well as Valiant's model of approximately correct learning.