Development of New Machine Learning Models Based on Gaussian Processes. Applications to Remote Sensing and Astrophysics
Metadata
Show full item recordAuthor
Morales Álvarez, PabloEditorial
Universidad de Granada
Departamento
Universidad de Granada. Programa de Doctorado en Física y MatemáticasMateria
Gaussian Processes Machine learning Remote sensing Astrophysics
Date
2020Fecha lectura
2020-10-05Referencia bibliográfica
Morales Álvarez, Pablo. Development of New Machine Learning Models Based on Gaussian Processes. Applications to Remote Sensing and Astrophysics. Granada: Universidad de Granada, 2020. [http://hdl.handle.net/10481/63966]
Sponsorship
Tesis Univ. Granada.; Fundación La CaixaAbstract
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)