High-dimension low–sample size statistical analysis is becoming increasingly important in a wide range of applied contexts. In such situations, the popular support vector machine suffers from "data piling" at the margin, which can diminish generalizability. This leads naturally to the development of distance-weighted discrimination, which is based on second-order cone programming, a modern computationally intensive optimization method.