Towards Efficient Active Learning of PDFA
Resumen:
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.
| 2022 | |
| Agencia Nacional de Investigación e Innovación | |
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Artificial Intelligencece Active Learning Ciencias Naturales y Exactas Ciencias de la Computación e Información Ciencias de la Computación |
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| Inglés | |
| Agencia Nacional de Investigación e Innovación | |
| REDI | |
| https://hdl.handle.net/20.500.12381/595 | |
| Acceso abierto | |
| Reconocimiento 4.0 Internacional. (CC BY) |
| Sumario: | 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. |
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