SILVA, A. I. N.; http://lattes.cnpq.br/6880021659838110; SILVA, André Igor Nóbrega.
Resumen:
Current expectations involving Artificial Intelligence and Machine Learning are high —
both in the scientific community and in the industry. The idea that we can extract complex
functions from a large amount of data is inspiring and leads us to believe that we can
improve our ability to predict future events and make better decisions using a data-driven
methodology. Within this context, we can use the Gaussian Regression Process algorithm
to perform supervised learning tasks in time series. It is a non-linear and non-parametric
regression algorithm based on Bayesian learning. In this we study the main characteristics
of the Gaussian Regression Process, focusing on the properties of its kernel functions.
3 basic problems are analyzed allowing us to visualize the algorithm training steps and
the distinctions between such kernel functions. Then, we apply it in practical problem to
predict the evolution of Covid-19 cases in the city of Campina Grande - PB.