Development of New Machine Learning Models Based on Gaussian Processes. Applications to Remote Sensing and Astrophysics

  1. Morales Álvarez, Pablo
Dirixida por:
  1. Rafael Molina Soriano Director
  2. Aggelos K. Katsaggelos Director

Universidade de defensa: Universidad de Granada

Fecha de defensa: 05 de outubro de 2020

Tribunal:
  1. Javier Mateos Delgado Presidente
  2. Mari Luz García Martínez Secretario/a
  3. Sandra Morales Martínez Vogal
  4. Juan Gabriel Serra Pérez Vogal
  5. Valeriana Naranjo Ornedo Vogal
Departamento:
  1. CIENCIAS DE LA COMPUTACIÓN E INTELIGENCIA ARTIFICIAL

Tipo: Tese

Resumo

In this PhD thesis we have developed different machine learning models based on Gaussian Processes. Different settings (regression, classification and crowdsourcing) are considered, and various application fields (specially remote sensing and astrophysics, but also threat detection and sentiment analysis) are targeted. The main global conclusion of this PhD thesis is the versatility of Gaussian Processes to model different scenarios (regression, classification, crowdsourcing) and target various applications (remote sensing, security, astrophysics), either as the central algorithm to perform the task at hand (Chapters 2-7) or as an auxiliary tool to be integrated within a larger model (Chapter 8)