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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.00245 |
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| _version_ | 1866929403951316992 |
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| author | Piotrovskaya, Ekaterina Lobski, Leo Zanasi, Fabio |
| author_facet | Piotrovskaya, Ekaterina Lobski, Leo Zanasi, Fabio |
| contents | We develop a learning algorithm for closed signal flow graphs - a graphical model of signal transducers. The algorithm relies on the correspondence between closed signal flow graphs and weighted finite automata on a singleton alphabet. We demonstrate that this procedure results in a genuine reduction of complexity: our algorithm fares better than existing learning algorithms for weighted automata restricted to the case of a singleton alphabet. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00245 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Closed Signal Flow Graphs Piotrovskaya, Ekaterina Lobski, Leo Zanasi, Fabio Logic in Computer Science Machine Learning 68Q45 F.1.1; D.3.1 We develop a learning algorithm for closed signal flow graphs - a graphical model of signal transducers. The algorithm relies on the correspondence between closed signal flow graphs and weighted finite automata on a singleton alphabet. We demonstrate that this procedure results in a genuine reduction of complexity: our algorithm fares better than existing learning algorithms for weighted automata restricted to the case of a singleton alphabet. |
| title | Learning Closed Signal Flow Graphs |
| topic | Logic in Computer Science Machine Learning 68Q45 F.1.1; D.3.1 |
| url | https://arxiv.org/abs/2407.00245 |