Results of the PAutomaC Probabilistic Automaton Learning Competition

Sicco Verwer, Rémi Eyraud, Colin Higuera
Proceedings of the Eleventh International Conference on Grammatical Inference, PMLR 21:243-248, 2012.

Abstract

Approximating distributions over strings is a hard learning problem. Typical GI techniques involve using finite state machines as models and attempting to learn both the structure and the weights, simultaneously. The PAutomaC competition is the first challenge to allow comparison between methods and algorithms and builds a first state of the art for these techniques. Both artificial data and real data were proposed and contestants were to try to estimate the probabilities of test strings. The purpose of this paper is to provide an overview of the implementation details of PAutomaC and to report the final results of the competition.

Cite this Paper

BibTeX
@InProceedings{pmlr-v21-verwer12a, title = {Results of the PAutomaC Probabilistic Automaton Learning Competition}, author = {Verwer, Sicco and Eyraud, Rémi and Higuera, Colin}, booktitle = {Proceedings of the Eleventh International Conference on Grammatical Inference}, pages = {243--248}, year = {2012}, editor = {Heinz, Jeffrey and Higuera, Colin and Oates, Tim}, volume = {21}, series = {Proceedings of Machine Learning Research}, address = {University of Maryland, College Park, MD, USA}, month = {05--08 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v21/verwer12a/verwer12a.pdf}, url = {https://proceedings.mlr.press/v21/verwer12a.html}, abstract = {Approximating distributions over strings is a hard learning problem. Typical GI techniques involve using finite state machines as models and attempting to learn both the structure and the weights, simultaneously. The PAutomaC competition is the first challenge to allow comparison between methods and algorithms and builds a first state of the art for these techniques. Both artificial data and real data were proposed and contestants were to try to estimate the probabilities of test strings. The purpose of this paper is to provide an overview of the implementation details of PAutomaC and to report the final results of the competition.} }
Endnote
%0 Conference Paper %T Results of the PAutomaC Probabilistic Automaton Learning Competition %A Sicco Verwer %A Rémi Eyraud %A Colin Higuera %B Proceedings of the Eleventh International Conference on Grammatical Inference %C Proceedings of Machine Learning Research %D 2012 %E Jeffrey Heinz %E Colin Higuera %E Tim Oates %F pmlr-v21-verwer12a %I PMLR %P 243--248 %U https://proceedings.mlr.press/v21/verwer12a.html %V 21 %X Approximating distributions over strings is a hard learning problem. Typical GI techniques involve using finite state machines as models and attempting to learn both the structure and the weights, simultaneously. The PAutomaC competition is the first challenge to allow comparison between methods and algorithms and builds a first state of the art for these techniques. Both artificial data and real data were proposed and contestants were to try to estimate the probabilities of test strings. The purpose of this paper is to provide an overview of the implementation details of PAutomaC and to report the final results of the competition.
RIS
TY - CPAPER TI - Results of the PAutomaC Probabilistic Automaton Learning Competition AU - Sicco Verwer AU - Rémi Eyraud AU - Colin Higuera BT - Proceedings of the Eleventh International Conference on Grammatical Inference DA - 2012/08/16 ED - Jeffrey Heinz ED - Colin Higuera ED - Tim Oates ID - pmlr-v21-verwer12a PB - PMLR DP - Proceedings of Machine Learning Research VL - 21 SP - 243 EP - 248 L1 - http://proceedings.mlr.press/v21/verwer12a/verwer12a.pdf UR - https://proceedings.mlr.press/v21/verwer12a.html AB - Approximating distributions over strings is a hard learning problem. Typical GI techniques involve using finite state machines as models and attempting to learn both the structure and the weights, simultaneously. The PAutomaC competition is the first challenge to allow comparison between methods and algorithms and builds a first state of the art for these techniques. Both artificial data and real data were proposed and contestants were to try to estimate the probabilities of test strings. The purpose of this paper is to provide an overview of the implementation details of PAutomaC and to report the final results of the competition. ER -
APA
Verwer, S., Eyraud, R. & Higuera, C.. (2012). Results of the PAutomaC Probabilistic Automaton Learning Competition. Proceedings of the Eleventh International Conference on Grammatical Inference, in Proceedings of Machine Learning Research 21:243-248 Available from https://proceedings.mlr.press/v21/verwer12a.html.

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