Learning by Transduction

TL;DR

This paper introduces a transductive learning method based on a modified support-vector machine that provides both predictions and confidence measures, supported by experimental results and potential extensions.

Contribution

It presents a novel transductive approach modifying SVMs to include confidence measures and evidence evaluation for predictions.

Findings

Method provides confidence levels for predictions.

Experimental results demonstrate effectiveness.

Discusses extensions for improved performance.

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.