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Learners Based on Transducers

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Language and Automata Theory and Applications (LATA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10792))

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Abstract

As data come out one by one from an infinite stream, automatic learners maintain some string as long term memory, and update it at every new datum (example) they process. Transduced learners are generalization of automatic learners. Both kind of learners are evaluated with respect to the space they consume for learning. For automatic learners, it is unknown whether at any point, the size of the long term memory can be bounded by the length of the longest datum that has been received so far. Here it is shown that, even when restricting learning to automatic families, there is a hierarchy of classes that can be learnt with memory \(O(n^k)\), and all automatic families which are learnable in principle can be learnt by a transduced learner using exponential sized memory.

S. Jain and F. Stephan are supported in part by the Singapore Ministry of Education Academic Research Fund grants R146-000-181-112 and MOE2016-T2-1-019 / R146-000-234-112. Furthermore, S. Jain is supported in part by NUS grant C252-000-087-001. F. Stephan did part of the work while on sabbatical leave to UNSW Sydney.

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Authors and Affiliations

  1. Department of Computer Science, National University of Singapore, 13 Computing Drive, COM1, Singapore, 117417, Republic of Singapore

    Sanjay Jain & Frank Stephan

  2. Department of Mathematics, National University of Singapore, 10 Lower Kent Ridge Road, S17, Singapore, 119076, Republic of Singapore

    Shao Ning Kuek & Frank Stephan

  3. School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia

    Eric Martin

Authors
  1. Sanjay Jain
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  2. Shao Ning Kuek
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  3. Eric Martin
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  4. Frank Stephan
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Correspondence to Sanjay Jain .

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Editors and Affiliations

  1. Bar-Ilan University, Ramat Gan, Israel

    Shmuel Tomi Klein

  2. Rovira i Virgili University, Tarragona, Spain

    Carlos Martín-Vide

  3. Ariel University, Ariel, Israel

    Dana Shapira

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Jain, S., Kuek, S.N., Martin, E., Stephan, F. (2018). Learners Based on Transducers. In: Klein, S., Martín-Vide, C., Shapira, D. (eds) Language and Automata Theory and Applications. LATA 2018. Lecture Notes in Computer Science(), vol 10792. Springer, Cham. https://doi.org/10.1007/978-3-319-77313-1_13

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  • DOI: https://doi.org/10.1007/978-3-319-77313-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77312-4

  • Online ISBN: 978-3-319-77313-1

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