Chapter 19. Large-Scale Machine Learning
Jerod J. Weinman, Augustus Lidaka and Shitanshu Aggarwal
A typical machine-learning algorithm creates a classification function that inductively generalizes from training examples — input features and associated classification labels — to previously unseen examples requiring labels. Optimizing the prediction accuracy of the learned function for complex problems can require massive amounts of training data. This chapter describes a GPU-based implementation of a discriminative maximum entropy learning algorithm that can improve runtime on large datasets by a factor of over 200.
Machine learning is used on a variety of problems, including time series prediction for financial forecasting ...