In bioinformatics studies, supervised classification with high-dimensional input variables is
frequently encountered. Examples routinely arise in genomic, epigenetic and proteomic …

Data mining techniques often ask for the resolution of optimization problems. Supervised
classification, and, in particular, support vector machines, can be seen as a paradigmatic …

Statistical Foundations of Data Science gives a thorough introduction to commonly used
statistical models, contemporary statistical machine learning techniques and algorithms …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

After its inception in Koenker and Bassett (1978), quantile regression has become an
important and widely used technique to study the whole conditional distribution of a …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

The support vector machine (SVM) has been widely applied for classification problems in
both machine learning and statistics. Despite its popularity, however, SVM has some …

Sparse optimization refers to an optimization problem involving the zero-norm in objective or
constraints. In this paper, nonconvex approximation approaches for sparse optimization …

Motivation: Given the thousands of genes and the small number of samples, gene selection
has emerged as an important research problem in microarray data analysis. Support Vector …

This paper proposes a robust classification model, based on support vector machine (SVM),
which simultaneously deals with outliers detection and feature selection. The classifier is …