Learning by transduction

Authors:
A. Gammerman;V. Vovk;V. Vapnik
Affiliations:
Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, UK;Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, UK;Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, UK
Venue:
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Year:
1998

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Abstract

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.