Learning by Transduction

Understand

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.