Learning Deterministic Finite Automata from Innite Alphabets

Nino Pellegrino; Christian Hammerschmidt; Sicco Verwer; Qin Lin

Learning Deterministic Finite Automata from Innite Alphabets

Nino Pellegrino, Christian Hammerschmidt, Sicco Verwer, Qin Lin

Cyber Security

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

Abstract

We proposes an algorithm to learn automata innite alphabets, or at least too large to enumerate. We apply it to dene a generic model intended for regression, with transitions constrained by intervals over the alphabet. The algorithm is based on the Red & Blue framework for learning from an input sample. We show two small case studies where the alphabets are respectively the natural and real numbers, and show how nice properties of automata models like interpretability and graphical representation transfer to regression where typical models are hard to interpret.

Original language	English
Title of host publication	Proceedings of The 13th International Conference on Grammatical Inference
Subtitle of host publication	The JMLR Workshop and Conference, The Sequence PredictIction ChallengE (SPiCe)
Editors	S. Verwer, M. van Zaanen, R. Smetsers
Publisher	JMLR
Pages	69-72
Number of pages	5
Volume	57
Publication status	Published - 2016
Event	13th International Conference on Grammatical Inference, ICGI 2016 - Delft, Netherlands Duration: 5 Oct 2016 → 7 Oct 2016

Publication series

Name	JMLR: Workshop and Conference Proceedings
ISSN (Electronic)	1938-7228

Conference

Conference	13th International Conference on Grammatical Inference, ICGI 2016
Abbreviated title	ICGI
Country/Territory	Netherlands
City	Delft
Period	5/10/16 → 7/10/16

Keywords

Passive Learning
Deterministic Finite Automata
Regression

Cite this

Pellegrino, N., Hammerschmidt, C., Verwer, S., & Lin, Q. (2016). Learning Deterministic Finite Automata from Innite Alphabets. In S. Verwer, M. van Zaanen, & R. Smetsers (Eds.), Proceedings of The 13th International Conference on Grammatical Inference: The JMLR Workshop and Conference, The Sequence PredictIction ChallengE (SPiCe) (Vol. 57, pp. 69-72). (JMLR: Workshop and Conference Proceedings). JMLR.

Pellegrino, Nino ; Hammerschmidt, Christian ; Verwer, Sicco et al. / Learning Deterministic Finite Automata from Innite Alphabets. Proceedings of The 13th International Conference on Grammatical Inference: The JMLR Workshop and Conference, The Sequence PredictIction ChallengE (SPiCe). editor / S. Verwer ; M. van Zaanen ; R. Smetsers. Vol. 57 JMLR, 2016. pp. 69-72 (JMLR: Workshop and Conference Proceedings).

@inproceedings{c5f3e59b8c454f9985c6fd456b960ff1,

title = "Learning Deterministic Finite Automata from Innite Alphabets",

abstract = "We proposes an algorithm to learn automata innite alphabets, or at least too large to enumerate. We apply it to dene a generic model intended for regression, with transitions constrained by intervals over the alphabet. The algorithm is based on the Red & Blue framework for learning from an input sample. We show two small case studies where the alphabets are respectively the natural and real numbers, and show how nice properties of automata models like interpretability and graphical representation transfer to regression where typical models are hard to interpret.",

keywords = "Passive Learning, Deterministic Finite Automata, Regression",

author = "Nino Pellegrino and Christian Hammerschmidt and Sicco Verwer and Qin Lin",

year = "2016",

language = "English",

volume = "57",

series = "JMLR: Workshop and Conference Proceedings",

publisher = "JMLR",

pages = "69--72",

editor = "S. Verwer and {van Zaanen}, M. and R. Smetsers",

booktitle = "Proceedings of The 13th International Conference on Grammatical Inference",

note = "13th International Conference on Grammatical Inference, ICGI 2016, ICGI ; Conference date: 05-10-2016 Through 07-10-2016",

}

Pellegrino, N, Hammerschmidt, C , Verwer, S & Lin, Q 2016, Learning Deterministic Finite Automata from Innite Alphabets. in S Verwer, M van Zaanen & R Smetsers (eds), Proceedings of The 13th International Conference on Grammatical Inference: The JMLR Workshop and Conference, The Sequence PredictIction ChallengE (SPiCe). vol. 57, JMLR: Workshop and Conference Proceedings, JMLR, pp. 69-72, 13th International Conference on Grammatical Inference, ICGI 2016, Delft, Netherlands, 5/10/16.

Learning Deterministic Finite Automata from Innite Alphabets. / Pellegrino, Nino; Hammerschmidt, Christian ; Verwer, Sicco et al.
Proceedings of The 13th International Conference on Grammatical Inference: The JMLR Workshop and Conference, The Sequence PredictIction ChallengE (SPiCe). ed. / S. Verwer; M. van Zaanen; R. Smetsers. Vol. 57 JMLR, 2016. p. 69-72 (JMLR: Workshop and Conference Proceedings).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Learning Deterministic Finite Automata from Innite Alphabets

AU - Pellegrino, Nino

AU - Hammerschmidt, Christian

AU - Verwer, Sicco

AU - Lin, Qin

PY - 2016

Y1 - 2016

N2 - We proposes an algorithm to learn automata innite alphabets, or at least too large to enumerate. We apply it to dene a generic model intended for regression, with transitions constrained by intervals over the alphabet. The algorithm is based on the Red & Blue framework for learning from an input sample. We show two small case studies where the alphabets are respectively the natural and real numbers, and show how nice properties of automata models like interpretability and graphical representation transfer to regression where typical models are hard to interpret.

AB - We proposes an algorithm to learn automata innite alphabets, or at least too large to enumerate. We apply it to dene a generic model intended for regression, with transitions constrained by intervals over the alphabet. The algorithm is based on the Red & Blue framework for learning from an input sample. We show two small case studies where the alphabets are respectively the natural and real numbers, and show how nice properties of automata models like interpretability and graphical representation transfer to regression where typical models are hard to interpret.

KW - Passive Learning

KW - Deterministic Finite Automata

KW - Regression

M3 - Conference contribution

VL - 57

T3 - JMLR: Workshop and Conference Proceedings

SP - 69

EP - 72

BT - Proceedings of The 13th International Conference on Grammatical Inference

A2 - Verwer, S.

A2 - van Zaanen, M.

A2 - Smetsers, R.

PB - JMLR

T2 - 13th International Conference on Grammatical Inference, ICGI 2016

Y2 - 5 October 2016 through 7 October 2016

ER -

Pellegrino N, Hammerschmidt C , Verwer S, Lin Q. Learning Deterministic Finite Automata from Innite Alphabets. In Verwer S, van Zaanen M, Smetsers R, editors, Proceedings of The 13th International Conference on Grammatical Inference: The JMLR Workshop and Conference, The Sequence PredictIction ChallengE (SPiCe). Vol. 57. JMLR. 2016. p. 69-72. (JMLR: Workshop and Conference Proceedings).

Learning Deterministic Finite Automata from Innite Alphabets

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