  We describe a method for predicting a classification of an object given
classifications of the objects in the training set, assuming that the pairs
object/classification are generated by an i.i.d. process from a continuous
probability distribution. Our method is a modification of Vapnik's
support-vector machine; its main novelty is that it gives not only the
prediction itself but also a practicable measure of the evidence found in
support of that prediction. We also describe a procedure for assigning degrees
of confidence to predictions made by the support vector machine. Some
experimental results are presented, and possible extensions of the algorithms
are discussed.
