Contribution of Probabilistic Grammar Inference with K-Testable Language for Knowledge Modeling: Application on aging peopleReport as inadecuate




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1 LHC - Laboratoire Hubert Curien Saint Etienne 2 UJM - Université Jean Monnet Saint-Etienne

Abstract : We investigate the contribution of unsupervised learning and regular grammatical inference to respectively identify profiles of elderly people and their development over time in order to evaluate care needs human, financial and physical resources. The proposed approach is based on k-Testable Languages in the Strict Sense Inference algorithm in order to infer a probabilistic automaton from which a Markovian model which has a discrete finite or countable state-space has been deduced. In simulating the corresponding Markov chain model, it is possible to obtain information on population ageing. We have verified if our observed system conforms to a unique long term state vector, called the stationary distribution and the steady-state.

Keywords : evolution of elderly people disability. grammar inference k-testable language in strict sense probabilistic deterministic finite automata time series evolution of elderly people disability





Author: Catherine Combes - Jean Azéma -

Source: https://hal.archives-ouvertes.fr/



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