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arXiv · 1301.7375

Learning by Transduction

Alex Gammerman·Volodya Vovk·Vladimir Vapnik
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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.

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Alex Gammerman, Volodya Vovk, Vladimir Vapnik. 2013-01-30. Learning by Transduction. https://arxiv.org/abs/1301.7375

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