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Towards Efficient Active Learning of PDFA

2022-06-17 20:48:58
Franz Mayr, Sergio Yovine, Federico Pan, Nicolas Basset, Thao Dang
arXiv_AI
     
Abstract

We propose a new active learning algorithm for PDFA based on three main aspects: a congruence over states which takes into account next-symbol probability distributions, a quantization that copes with differences in distributions, and an efficient tree-based data structure. Experiments showed significant performance gains with respect to reference implementations.

Abstract (translated)

URL

https://arxiv.org/abs/2206.09004

PDF

https://arxiv.org/pdf/2206.09004.pdf

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