Actively Learning Probabilistic Subsequential Transducers

Hasan Ibne Akram, Colin La Higuera, Claudia Eckert
Proceedings of the Eleventh International Conference on Grammatical Inference, PMLR 21:19-33, 2012.

Abstract

In this paper we investigate learning of probabilistic subsequential transducers in an active learning environment. In our learning algorithm the learner interacts with an oracle by asking probabilistic queries on the observed data. We prove our algorithm in an identification in the limit model. We also provide experimental evidence to show the correctness and to analyze the learnability of the proposed algorithm.

Cite this Paper

BibTeX
@InProceedings{pmlr-v21-akram12a, title = {Actively Learning Probabilistic Subsequential Transducers}, author = {Akram, Hasan Ibne and La Higuera, Colin and Eckert, Claudia}, booktitle = {Proceedings of the Eleventh International Conference on Grammatical Inference}, pages = {19--33}, 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/akram12a/akram12a.pdf}, url = {https://proceedings.mlr.press/v21/akram12a.html}, abstract = {In this paper we investigate learning of probabilistic subsequential transducers in an active learning environment. In our learning algorithm the learner interacts with an oracle by asking probabilistic queries on the observed data. We prove our algorithm in an identification in the limit model. We also provide experimental evidence to show the correctness and to analyze the learnability of the proposed algorithm.} }
Endnote
%0 Conference Paper %T Actively Learning Probabilistic Subsequential Transducers %A Hasan Ibne Akram %A Colin La Higuera %A Claudia Eckert %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-akram12a %I PMLR %P 19--33 %U https://proceedings.mlr.press/v21/akram12a.html %V 21 %X In this paper we investigate learning of probabilistic subsequential transducers in an active learning environment. In our learning algorithm the learner interacts with an oracle by asking probabilistic queries on the observed data. We prove our algorithm in an identification in the limit model. We also provide experimental evidence to show the correctness and to analyze the learnability of the proposed algorithm.
RIS
TY - CPAPER TI - Actively Learning Probabilistic Subsequential Transducers AU - Hasan Ibne Akram AU - Colin La Higuera AU - Claudia Eckert 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-akram12a PB - PMLR DP - Proceedings of Machine Learning Research VL - 21 SP - 19 EP - 33 L1 - http://proceedings.mlr.press/v21/akram12a/akram12a.pdf UR - https://proceedings.mlr.press/v21/akram12a.html AB - In this paper we investigate learning of probabilistic subsequential transducers in an active learning environment. In our learning algorithm the learner interacts with an oracle by asking probabilistic queries on the observed data. We prove our algorithm in an identification in the limit model. We also provide experimental evidence to show the correctness and to analyze the learnability of the proposed algorithm. ER -
APA
Akram, H.I., La Higuera, C. & Eckert, C.. (2012). Actively Learning Probabilistic Subsequential Transducers. Proceedings of the Eleventh International Conference on Grammatical Inference, in Proceedings of Machine Learning Research 21:19-33 Available from https://proceedings.mlr.press/v21/akram12a.html.

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