Learning Product Automata

Joshua Moerman

doi:10.48550/arXiv.1705.02850

Learning Product Automata

Joshua Moerman^*

^*Corresponding author for this work

Research output: Working paper / Preprint › Preprint › Academic

Abstract

In this paper we give an optimization for active learning algorithms, applicable to learning Moore machines where the output comprises several observables. These machines can be decomposed themselves by projecting on each observable, resulting in smaller components. These components can then be learnt with fewer queries. This is in particular interesting for learning software, where compositional methods are important for guaranteeing scalability.

Original language	English
Publisher	Cornell University - arXiv
Number of pages	5
DOIs	https://doi.org/10.48550/arXiv.1705.02850
Publication status	Published - May 2017
Externally published	Yes

Publication series

Series	Computing Research Repository
ISSN	2331-8422

Access to Document

10.48550/arXiv.1705.02850

1 Conference Article in proceeding

Learning Product Automata
Moerman, J., 2019, Proceedings of Machine Learning Research. Unold, O., Dyrka, W. & Wieczorek, W. (eds.). PMLR, Vol. 93. p. 54-66 13 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

Open Access

Cite this

@techreport{f520c0136db44433a905a803e67581f7,

title = "Learning Product Automata",

abstract = "In this paper we give an optimization for active learning algorithms, applicable to learning Moore machines where the output comprises several observables. These machines can be decomposed themselves by projecting on each observable, resulting in smaller components. These components can then be learnt with fewer queries. This is in particular interesting for learning software, where compositional methods are important for guaranteeing scalability.",

author = "Joshua Moerman",

note = "DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.",

year = "2017",

month = may,

doi = "10.48550/arXiv.1705.02850",

language = "English",

series = "Computing Research Repository",

publisher = "Cornell University - arXiv",

address = "United States",

type = "WorkingPaper",

institution = "Cornell University - arXiv",

}

TY - UNPB

T1 - Learning Product Automata

AU - Moerman, Joshua

N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2017/5

Y1 - 2017/5

N2 - In this paper we give an optimization for active learning algorithms, applicable to learning Moore machines where the output comprises several observables. These machines can be decomposed themselves by projecting on each observable, resulting in smaller components. These components can then be learnt with fewer queries. This is in particular interesting for learning software, where compositional methods are important for guaranteeing scalability.

AB - In this paper we give an optimization for active learning algorithms, applicable to learning Moore machines where the output comprises several observables. These machines can be decomposed themselves by projecting on each observable, resulting in smaller components. These components can then be learnt with fewer queries. This is in particular interesting for learning software, where compositional methods are important for guaranteeing scalability.

U2 - 10.48550/arXiv.1705.02850

DO - 10.48550/arXiv.1705.02850

M3 - Preprint

T3 - Computing Research Repository

BT - Learning Product Automata

PB - Cornell University - arXiv

ER -

Learning Product Automata

Abstract

Publication series

Access to Document

Fingerprint

Research output

Learning Product Automata

Cite this