Brian Ripley
Oxford University
Abstract
Nearest-neighbour algorithms in pattern recognition are almost always
amongst the best in comparative studies; they are known by many names
in other fields but all work by examining the few most similar examples
from the training set. There are now ways to use data to help decide
the metric for `nearest' and many relationships to visualization
algorithms. There are some circumstances in which they fail bbadly,
but often can be improved by `editing' algorithms.