Statistical Learning for Biomedical Data

Learning, machines come from the world of probability and computer science, but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in non-technical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable, traditional models.

Coverage includes single decision trees, multiple-tree techniques such as Random Forests�, neural nets, support vector machines, nearest neighbors, and boosting.