Support vector regression

Abstract This chapter provides an overview of the support vector regression (SVR), an analytical technique to investigate the relationship between one or more predictor variables and a real-valued (continuous) dependent variable. In the first part of the chapter, we provide a description of the SVR algorithm. Unlike traditional regression methods that depend on assumptions of the model that might not be accurate (e.g., linear data distribution), SVR is a machine learning technique in which a model learns a variable's importance for characterizing the relationship between input and output. In the second part of the chapter, we review a number of studies that have applied SVR to magnetic resonance imaging data to performance multivariate pattern regression analysis of brain disorders. These studies have been successful in revealing spatially distributed patterns across multiple brain regions in several brain disorders including schizophrenia, autism, and attention-deficit/hyperactivity disorder.