Understanding and Scaling-Up Machine Learning Algorithms

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Dietterich Understanding and Scaling-Up Machine Learning Algorithms Several machine learning algorithms have become very popular and widely used -- particularly the top-down partitioning algorithms for decision trees and the backpropagation algorithm for feed-forward neural networks. This research seeks to deepen our understanding of these algorithms, to explain why various boosting and voting techniques improve their accuracy, and to determine the best procedures for applying them in practice. A second goal of the research is to find ways of scaling up these algorithms (and their associated boosting techniques) to handle problems with billions of training examples and thousands of output classes. Based on our experience with voting algorithms, we will develop methods for reducing the cost of voting, scaling up the number of output classes, handling continuous output values, and handling large numbers of training examples. Existing learning algorithms have been designed for conditions where data is very expensive and computer time is relatively cheap. Emerging applications in data mining exhibit the opposite conditions: data is voluminous and users want interactive speeds. This research will result in new algorithms that are essential to supporting emerging applications in large-scale data mining.