Learning by Transduction
Alex Gammerman, Volodya Vovk, Vladimir Vapnik
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.
Keywords: Transduction, Support Vector Machine, confidence.
PDF Link: /papers/98/p148-gammerman.pdf
AUTHOR = "Alex Gammerman
and Volodya Vovk and Vladimir Vapnik",
TITLE = "Learning by Transduction",
BOOKTITLE = "Proceedings of the Fourteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-98)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1998",
PAGES = "148--155"