Apprentissage statistique sur données longitudinales de grande taille et applications au design des jeux vidéo

Abstract : This thesis focuses on longitudinal time to event data possibly large along the following tree axes : number of individuals, observation frequency and number of covariates. We introduce a penalised estimator based on Cox complete likelihood with data driven weights. We introduce proximal optimization algorithms to efficiently fit models coefficients. We have implemented thoses methods in C++ and in the R package coxtv to allow everyone to analyse data sets bigger than RAM; using data streaming and online learning algorithms such that proximal stochastic gradient descent with adaptive learning rates. We illustrate performances on simulations and benchmark with existing models. Finally, we investigate the issue of video game design. We show that using our model on large datasets available in video game industry allows us to bring to light ways of improving the design of studied games. First we have a look at low level covariates, such as equipment choices through time and show that this model allows us to quantify the effect of each game elements, giving to designers ways to improve the game design. Finally, we show that the model can be used to extract more general design recommendations such as dificulty influence on player motivations.
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Thibault Allart. Apprentissage statistique sur données longitudinales de grande taille et applications au design des jeux vidéo. Traitement du signal et de l'image [eess.SP]. Conservatoire national des arts et metiers - CNAM, 2017. Français. ⟨NNT : 2017CNAM1136⟩. ⟨tel-01683136⟩

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