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arXiv Machine Learning By Yong Li, Qiyi Tang, Di-De Yen

Learning Canonical Register Automata over Ordered Data Domains

Read the original on arXiv Machine Learning →

The paper presents a polynomial‑time active learning procedure for deterministic register automata (DRAs) over ordered data domains, covering both dense domains like the rationals and non‑dense domains such as the integers. It unifies the learning framework for these domains using membership, equivalence, and memorability queries, and shows that minimization of DRAs over the integer domain is decidable. Additionally, the authors provide improved complexity bounds for several decision problems related to DRAs over ordered domains.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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