Kernels and Ensembles: Perspectives on Statistical Learning

Details Kernels and Ensembles: Perspectives on Statistical Learning Kernels and Ensembles: Perspectives on Statistical Learning

2007-12-06

arxiv.org/abs/0712.1027v1

The American Statistician, May 2008, Vol. 62, No. 2, Pages 97 - 109.

Statistics

Methodology

22 pages; 6 figures; sumitted to The American Statistician

Scientific paper

10.1198/000313008X306367

Since their emergence in the 1990's, the support vector machine and the AdaBoost algorithm have spawned a wave of research in statistical machine learning. Much of this new research falls into one of two broad categories: kernel methods and ensemble methods. In this expository article, I discuss the main ideas behind these two types of methods, namely how to transform linear algorithms into nonlinear ones by using kernel functions, and how to make predictions with an ensemble or a collection of models rather than a single model. I also share my personal perspectives on how these ideas have influenced and shaped my own research. In particular, I present two recent algorithms that I have invented with my collaborators: LAGO, a fast kernel algorithm for unbalanced classification and rare target detection; and Darwinian evolution in parallel universes, an ensemble method for variable selection.

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