Reading for Gradient Boosting Machine
Basically, we'll read a series of three papers in order to get a complete understanding of gbm. The sequence follows the temporal order in which these papers were published and will allow us to follow the development of the idea. In order, here are the papers.
1. additiveLogisticRegression-Boosting.pdf
2. greedyFunctionApprox-2001.pdf
3. stochasticGradientBoosting-2002.pdf
Also look at the gbm package guide gbmPackageGuide.pdf This has got some general explanation of gbm in addition to descriptions of the packages features and options.
Here's R-code for the in class iris example: gbm.R
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