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A new heuristic for inferring regular grammars.

¹Department of Information and Computer Science, University of Hawaii, Honolulu, HI 96822.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 27, 2011

Summary

Modified grammatical inference schemes show better performance on small datasets but are unsuitable for large ones. This study compares original and modified schemes using complexity measures.

Area of Science:

Computational Linguistics
Artificial Intelligence
Machine Learning

Background:

Grammatical inference is crucial for understanding and generating language.
Existing schemes, like Feldman et al.'s, have limitations in performance and scalability.
Complexity measures are vital for evaluating algorithmic efficiency.

Purpose of the Study:

To present modifications to Feldman et al.'s grammatical inference scheme.
To compare the performance of original and modified schemes using Feldman and Wharton's complexity measures.
To analyze the impact of sample set size on the modified scheme's effectiveness.

Main Methods:

Modifications were applied to an existing grammatical inference scheme.
Performance was evaluated using complexity measures.

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A specific case involving a complex model generating 104 samples was analyzed.

The algorithm's performance was tested with both small and large sample sets.

Main Results:

A set of 104 samples successfully trained the modified scheme to infer the original model's grammar.
The modified scheme demonstrated superior performance on small sample sets.
The modified scheme proved highly unsuitable for large sample sets, indicating scalability issues.

Conclusions:

The modified grammatical inference scheme offers advantages for small datasets.
Scalability remains a significant challenge for the modified scheme when dealing with large datasets.
Further research is needed to address the limitations of modified schemes in large-scale applications.