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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 How the Structure of the Constraint Space Enables Learning Chandlee, Jane; Eyraud, Remi; Heinz, Jeffrey... In: Proceedings of the Society for Computation in Linguistics (2019) BASE Show details 2 Markedness constraints are negative: An autosegmental constraint definition language Jardine, Adam; Heinz, Jeffrey In: Proceedings of the fifty-first (51.) annual meeting of the Chicago Linguistic Society (2016), S. 301-316 Leibniz-Zentrum Allgemeine Sprachwissenschaft Show details 3 Learning Repairs for Marked Structures Chandlee, Jane; Jardine, Adam; Heinz, Jeffrey In: Proceedings of the Annual Meetings on Phonology; Proceedings of the 2014 Annual Meeting on Phonology ; 2377-3324 (2016) BASE Show details 4 Very efficient learning of structured classes of subsequential functions from positive data Jardine, Adam; Chandlee, Jane; Eyraud, Rémi; Heinz, Jeffrey In: The 12th International Conference on Grammatical Inference ; https://hal.archives-ouvertes.fr/hal-01193057 ; The 12th International Conference on Grammatical Inference, Sep 2014, Kyoto, Japan. pp.94-108 ; http://jmlr.org/proceedings/papers/v34/ (2014) Abstract: International audience ; 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 a 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. Keyword: [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; identification with polynomial bounds; onwardness; transduction URL: https://hal.archives-ouvertes.fr/hal-01193057 https://hal.archives-ouvertes.fr/hal-01193057/document https://hal.archives-ouvertes.fr/hal-01193057/file/jardine14a.pdf BASE Hide details

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