Browsing Teses e dissertações by Advisor "3e57f161-19fe-4345-9e87-bc60eb7be98f"
Now showing items 1-10 of 10
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Small and time-efficient distribution-free predictive regions
(Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 02/05/2023)Predicting a target variable (response) is often the main objective of many studies and investigations. In such scenarios, there are usually other variables, known as covariates, that are more readily available and can ... -
Scalable and interpretable kernel methods based on random Fourier features
(Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 29/03/2023)Kernel methods are a class of statistical machine learning models based on positive semidefinite kernels, which serve as a measure of similarity between data features. Examples of kernel methods include kernel ridge ... -
Estimação de funções do redshift de galáxias com base em dados fotométricos
(Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 18/09/2017)In a substantial amount of astronomy problems, we are interested in estimating values assumed of some unknown quantity z ∈ R, for many function g, based on covariates x ∈ R^d. This is made using a sample (X1,Z1), ... ... -
Quantificação em problemas com mudança de domínio
(Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 17/05/2018)Several machine learning applications use classifiers as a way of quantifying the prevalence of positive class labels in a target dataset, a task named quantification. For instance, a naive way of determining what proportion ... -
Neural networks as an optimization tool for regression
(Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 02/09/2019)Neural networks are a tool to solve prediction problems that have gained much prominence recently. In general, neural networks are used as a predictive method, that is, their are used to estimate a regression function. ... -
Conditional independence testing, two sample comparison and density estimation using neural networks
(Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 03/08/2020)Given the vast amount of data available nowadays and the rapid increase of computational processing power, the field of machine learning and the so called algorithmic modeling have seen a recent surge in its popularity and ... -
Comparing two populations using Bayesian Fourier series density estimation
(Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 12/04/2017)Given two samples from two populations, one could ask how similar the populations are, that is, how close their probability distributions are. For absolutely continuous distributions, one way to measure the proximity of ... -
Vector representation of texts applied to prediction models
(Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 09/03/2020)Natural Language Processing has gone through substantial changes over time. It was only recently that statistical approaches started receiving attention. The Word2Vec model is one of these. It is a shallow neural network ... -
Multivariate conditional density estimation with copulas
(Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 29/09/2021)Most machine learning regression models only yield single point estimations for the label of a new observation. However, when dealing with multi-modal or asymmetric distributions, a single point estimate is not enough to ... -
Bandas de predição usando densidade condicional estimada e um modelo LDA com covariáveis
(Universidade Federal de São Carlos, UFSCar, Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs, Câmpus São Carlos, 15/10/2021)Machine learning methods are divided into two main groups: supervised and unsupervised methods. In the first part of this work, we develop a method for creating prediction bands that can be applied to supervised problems. ...