Information Processing Letters
Random DFA's can be approximately learned from sparse uniform examples
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A new algorithm for the reduction of incompletely specified finite state machines
Proceedings of the 1998 IEEE/ACM international conference on Computer-aided design
A sampling-based heuristic for tree search applied to grammar induction
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Formal languages and their relation to automata
Formal languages and their relation to automata
Inducing grammars from sparse data sets: a survey of algorithms and results
The Journal of Machine Learning Research
Learning Deterministic Finite Automata with a Smart State Labeling Evolutionary Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inducing Regular Languages Using Grammar-Based Classifier System
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
Universal automata and NFA learning
Theoretical Computer Science
Grammar-based classifier system: a universal tool for grammatical inference
WSEAS Transactions on Computers
Language structure using fuzzy similarity
IEEE Transactions on Fuzzy Systems
Inference of regular languages using state merging algorithms with search
Pattern Recognition
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In this paper, we analyze the effectiveness of a leading finite state automaton (FSA) induction algorithm, windowed evidence driven state merging (W-EDSM). W-EDSM generates small automata that correctly label a given set of positive and a given set of negative example strings defined by a regular (Type 3) language. In particular, W-EDSM builds a prefix tree for the exemplars which is then collapsed into a FSA. This is done by selecting nodes to merge based on a simple heuristic until no more merges are possible. Our experimental results show that the heuristic used works well for later merges, but not very well for early merges. Based on this observation, we are able to make a small modification to W-EDSM which improves the performance of the algorithm by 27% and suggest other avenues for futher enhancement.