(9/1/00)

Many learning algorithms, especially nonparametric ones, use distance
measures as a source of prior knowledge about the domain. This paper shows
how the work of Baxter and Yianilos provides a formal equivalence between
distance measures and prior probability distributions in Bayesian
inference. The prior distribution applies either to how the data was
generated or to the shape of the discrimination boundary. This perspective
is useful for extending distance-based algorithms to new feature spaces and
especially for *learning* distance measures on those spaces.

Also see Learning distance measures from labeled data -- An overview.

Thomas P Minka Last modified: Thu Apr 22 12:19:33 GMT 2004