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This research advances theoretical machine learning by developing efficient algorithms for sequence prediction based on right-to-left automaticity and arithmetic repetition complexity, distinct from previous left-to-right approaches. The work addresses fundamental problems in formal languages and automata theory with direct implications for understanding the structure of mix-automatic sequences through novel computational measures.
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Stringological sequence prediction II: Right-to-left automaticity and related complexity measures

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2607.17369v1 Announce Type: cross Abstract: In a previous paper, we began the study of sequence prediction algorithms adapted to stringological word complexity measures. One measure we considered was left-to-right (most-significant-digit-first) automaticity. Here, we show a statistically and computationally efficient algorithm adapted to the ``dual'' right-to-left (least-significant-digit-first) automaticity, which turns out to be substantially different for our purpose. We also demonstrate a prediction algorithm for a more expressive measure that we call ``arithmetic repetition complexity''. In particular, the latter can be used for predicting the so-called mix-automatic sequences.

Abstract:In a previous paper, we began the study of sequence prediction algorithms adapted to stringological word complexity measures. One measure we considered was left-to-right (most-significant-digit-first) automaticity. Here, we show a statistically and computationally efficient algorithm adapted to the ``dual'' right-to-left (least-significant-digit-first) automaticity, which turns out to be substantially different for our purpose. We also demonstrate a prediction algorithm for a more expressive measure that we call ``arithmetic repetition complexity''. In particular, the latter can be used for predicting the so-called mix-automatic sequences.

Formal Languages and Automata Theory (cs.FL); Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)

I.2.6; F.2.2; G.2.1

arXiv:2607.17369 [cs.FL]

(or arXiv:2607.17369v1 [cs.FL] for this version)

https://doi.org/10.48550/arXiv.2607.17369 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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