Charles Taylor (University of Leeds):
Boosting kernel estimates
<br><Br>
Kivonat:
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 perfromance.
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.