Boosting kernel estimates
Seminar Room 2, Newton Institute Gatehouse
Kernel density estimation can be used to implement an estimate of Bayes' rule for classification. Kernel functions can also be used in nonparametric regression, and all three topics (classification, regression and clustering) are examples of "statistical learning". Boosting - an iterative procedure for improving estimates - is increasingly widely used due to its impressive performance. In this talk we give an introduction to these kernel methods as well as to boosting. We show how to implement boosting in each case, and illustrate (both theoretically, and by example) the effect on bias and variance.