Very efficient learning of structured classes of subsequential functions from positive data

Adam Jardine, Jane Chandlee, Rémi Eyraud, Jeffrey Heinz
; The 12th International Conference on Grammatical Inference, PMLR 34:94-108, 2014.

Abstract

In this paper, we present a new algorithm that can identify in polynomial time and data using positive examples any class of subsequential functions that share a particular finite-state structure. While this structure is given to the learner \textita priori, it allows for the exact learning of partial functions, and both the time and data complexity of the algorithm are linear. We demonstrate the algorithm on examples from natural language phonology and morphology in which the needed structure has been argued to be plausibly known in advance. A procedure for making any subsequential transducer onward without changing its structure is also presented.

Cite this Paper


BibTeX
@InProceedings{pmlr-v34-jardine14a, title = {Very efficient learning of structured classes of subsequential functions from positive data}, author = {Adam Jardine and Jane Chandlee and Rémi Eyraud and Jeffrey Heinz}, booktitle = {The 12th International Conference on Grammatical Inference}, pages = {94--108}, year = {2014}, editor = {Alexander Clark and Makoto Kanazawa and Ryo Yoshinaka}, volume = {34}, series = {Proceedings of Machine Learning Research}, address = {Kyoto, Japan}, month = {17--19 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v34/jardine14a.pdf}, url = {http://proceedings.mlr.press/v34/jardine14a.html}, abstract = {In this paper, we present a new algorithm that can identify in polynomial time and data using positive examples any class of subsequential functions that share a particular finite-state structure. While this structure is given to the learner \textita priori, it allows for the exact learning of partial functions, and both the time and data complexity of the algorithm are linear. We demonstrate the algorithm on examples from natural language phonology and morphology in which the needed structure has been argued to be plausibly known in advance. A procedure for making any subsequential transducer onward without changing its structure is also presented.} }
Endnote
%0 Conference Paper %T Very efficient learning of structured classes of subsequential functions from positive data %A Adam Jardine %A Jane Chandlee %A Rémi Eyraud %A Jeffrey Heinz %B The 12th International Conference on Grammatical Inference %C Proceedings of Machine Learning Research %D 2014 %E Alexander Clark %E Makoto Kanazawa %E Ryo Yoshinaka %F pmlr-v34-jardine14a %I PMLR %J Proceedings of Machine Learning Research %P 94--108 %U http://proceedings.mlr.press %V 34 %W PMLR %X In this paper, we present a new algorithm that can identify in polynomial time and data using positive examples any class of subsequential functions that share a particular finite-state structure. While this structure is given to the learner \textita priori, it allows for the exact learning of partial functions, and both the time and data complexity of the algorithm are linear. We demonstrate the algorithm on examples from natural language phonology and morphology in which the needed structure has been argued to be plausibly known in advance. A procedure for making any subsequential transducer onward without changing its structure is also presented.
RIS
TY - CPAPER TI - Very efficient learning of structured classes of subsequential functions from positive data AU - Adam Jardine AU - Jane Chandlee AU - Rémi Eyraud AU - Jeffrey Heinz BT - The 12th International Conference on Grammatical Inference PY - 2014/08/30 DA - 2014/08/30 ED - Alexander Clark ED - Makoto Kanazawa ED - Ryo Yoshinaka ID - pmlr-v34-jardine14a PB - PMLR SP - 94 DP - PMLR EP - 108 L1 - http://proceedings.mlr.press/v34/jardine14a.pdf UR - http://proceedings.mlr.press/v34/jardine14a.html AB - In this paper, we present a new algorithm that can identify in polynomial time and data using positive examples any class of subsequential functions that share a particular finite-state structure. While this structure is given to the learner \textita priori, it allows for the exact learning of partial functions, and both the time and data complexity of the algorithm are linear. We demonstrate the algorithm on examples from natural language phonology and morphology in which the needed structure has been argued to be plausibly known in advance. A procedure for making any subsequential transducer onward without changing its structure is also presented. ER -
APA
Jardine, A., Chandlee, J., Eyraud, R. & Heinz, J.. (2014). Very efficient learning of structured classes of subsequential functions from positive data. The 12th International Conference on Grammatical Inference, in PMLR 34:94-108

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