Learning regular sets from queries and counterexamples
Information and Computation
The nature of statistical learning theory
The nature of statistical learning theory
Phase transitions and the search problem
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Recent advances of grammatical inference
Theoretical Computer Science - Special issue on algorithmic learning theory
Phase Transitions in Relational Learning
Machine Learning
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
What Is the Search Space of the Regular Inference?
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
PAC Learning with Simple Examples
STACS '96 Proceedings of the 13th Annual Symposium on Theoretical Aspects of Computer Science
Inductive Inference, DFAs, and Computational Complexity
AII '89 Proceedings of the International Workshop on Analogical and Inductive Inference
Learning DFA from Simple Examples
ALT '97 Proceedings of the 8th International Conference on Algorithmic Learning Theory
Relational learning as search in a critical region
The Journal of Machine Learning Research
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An ever greater range of applications call for learning from sequences. Grammar induction is one prominent tool for sequence learning, it is therefore important to know its properties and limits. This paper presents a new type of analysis for inductive learning. A few years ago, the discovery of a phase transition phenomenon in inductive logic programming proved that fundamental characteristics of the learning problems may affect the very possibility of learning under very general conditions. We show that, in the case of grammatical inference, while there is no phase transition when considering the whole hypothesis space, there is a much more severe "gap" phenomenon affecting the effective search space of standard grammatical induction algorithms for deterministic finite automata (DFA). Focusing on standard search heuristics, we show that they overcome this difficulty to some extent, but that they are subject to overgeneralization. The paper last suggests some directions to alleviate this problem.