Machine Learning in Parallel with Support Vector Machines, Generalized Linear Models, and Adaptive Boosting

This article describes methods for machine learning using bootstrap samples and parallel processing to model very large volumes of data in short periods of time. The R programming language includes many packages for machine learning different types of data. Three of these packages include Support Vector Machines (SVM) [1], Generalized Linear Models (GLM) [2], and Adaptive Boosting (AdaBoost) [3]. While all three packages can be highly accurate for various types of classification problems, each package performs very differently when modeling (i.e. In particular, model fitting for Generalized Linear Models execute in much shorter periods of time than either Support Vector Machines or Adaptive Boosting.