Título: Matern Kernel for incomplete data

Data: 03/08/2023

Horário: 10h00

Local: Sala de Seminários - Bloco 952

Resumo:

Methods based on similarity measures such as distances or kernel functions are widely used in machine learning and related fields. These methods often take for granted that data is fully observed and are not equipped to handle incomplete data in an organic manner. This assumption is often flawed, as incomplete data is a fact in various domains such as medical diagnosis and sensor analytics. Therefore, one might find it useful to be able to estimate these kernels in the presence of partially observed data. In this work, we propose two methods to estimate the Matern Kernel in a direct way (i.e. without any preprocessing step).

Banca examinadora:

• Prof. Dr. João Paulo Pordeus Gomes (MDCC/UFC - Orientador)
• Prof. Dr. Cesar Lincoln Cavalcante Mattos (UFC - Coorientador)
• Prof. Dr. João Paulo do Vale Madeiro (UFC)
• Prof. Dr. Ajalmar Rego Rocha Neto (IFCE)