Eufrasio de Andrade Lima Neto

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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)
This paper presents a overview about the symbolic regression models to interval-valued data. The major symbolic regression methods proposed in literature visualized the problem like a optimization point of view. Lima Neto et. al. (2009) proposed a new symbolic regression model for interval variables, called bivariate generalized linear model (BGLM), which(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 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)