Letter from the editor-in-chief

Abstract

Journal of the Brazilian Computer Society objective is to publish original quality research papers, serving as a forum for promoting research in all fields of computer science. All submissions are peer reviewed by society members. This is a very relevant community service and it defines the journal's quality. Fortunately, the invited reviewers are being very helpful in accomplishing this task. The first paper in this issue is entitled "Statistical Learning Approaches for Discriminant Features Selection" by Giraldi, Rodrigues, Kitani, Sato and Thomaz. Statistical learning theory explores ways of estimating functional dependency from a given collection of data. The specific sub-area of supervised statistical learning covers important models like Perceptron, Support Vector Machines and Linear Discriminant Analysis. The authors review the theory of such models and compare their separating hypersurfaces for extracting group-differences between samples. Classification and reconstruction are the main goals of this comparison. Finally, they show recent advances illustrating their application on face and medical image databases.