Optimización bayesiana en técnicas de aprendizaje automàtico 'machine learning' clásicas: redes neuronales y XGBoost y su aplicación como modelos predictores de diabetes en pacientes ecuatorianos

Συγγραφείς: Borja Robalino, Ricardo Stalin

Συνεισφορές: University/Department: Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa

Thesis Advisors: Monleón Getino, Antonio, Gibert, Karina

Πηγή: TDX (Tesis Doctorals en Xarxa)

Θεματικοί όροι: Aprendizaje automático, Aprendizaje supervisado, Teorema de Bayes, Redes neuronales artificiales, XGBoost, Optimización bayesiana, Diabetes mellitus tipo 1, Diabetes mellitus tipo 2, Diabetes gestacional, Inferencia bayesiana, Machine learning, Bayesian inference, Supervised learning, Bayes' theorem, artificial neural networks, Bayesian optimization, Type 1 diabetes mellitus, Type 2 diabetes mellitus, Gestational diabetes, Aprenentatge automàtic, Inferència bayesiana, Aprenentatge supervisat, Xarxes neuronals artificials, Optimització bayesiana, Diabetis mellitus tipus 1, Diabetis mellitus tipus 2, Diabetis gestacional, Àrees temàtiques de la UPC::Matemàtiques i estadística, Àrees temàtiques de la UPC::Ciències de la salut, Àrees temàtiques de la UPC::Informàtica, 51 - Matemàtiques, 004 - Informàtica, 616.3 - Patologia de l'aparell digestiu. Odontologia

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: https://hdl.handle.net/10803/697249
https://dx.doi.org/10.5821/dissertation-2117-460594

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