Fast Committee Machines for Regression and Classification

Harris Drucker

In many data mining applications we are give a set of training examples and asked to construct a regression machine or a classifier that has low prediction error or low error rate on new examples, respectively. An important issue is speed especially when there are large amounts of data. We show how to both classification and prediction error can be reduced by using boosting techniques to implement committee machines. In our implementation of committees using either classification trees or regression trees, we show how we can trade off speed against either error rate or prediction error.

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