Learn More
This paper introduces a new approach to fitting a linear regression model to symbolic interval data. Each example of the learning set is described by a feature vector, for which each feature value is an interval. The new method fits a linear regression model on the mid-points and ranges of the interval values assumed by the variables in the learning set.(More)
Current symbolic regression methods visualize problems from an optimization point of view and do not consider the probabilistic aspects related to regression models. In this paper, we present the bivariate generalized linear model (BGLM) proposed by Iwasaki and Tsubaki [5] in the context of interval-valued data sets. Important aspects related to the BGLM(More)
The paper presents a meaningful learning tool known as Gowin's V and highlights the possibility of using it in the process of unpacking academic work in nursing. Our study aims to propose an amendment to this tool using the elements that comprise the procedural trajectory of the Theory of Nursing Praxis Intervention in Collective Health (TIPESC) and to(More)
This paper introduces a nonlinear regression model to interval-valued data. The method extends the classical nonlinear regression model in order to manage interval-valued datasets. The parameter estimates of the nonlinear model considers some optimization algorithms aiming to identify which one presents the best accuracy and precision in the prediction(More)
This paper introduce a new criterion and two new linear regression methods to predict interval-valued data. The proposed approaches consist in a new point of view to study the relationship between the midpoints and the ranges of the interval-valued variables. The evaluation of the proposed prediction methods is based on the average behaviour of the root(More)