Learning by Transduction
This work addresses the need for reliable confidence estimation in classification tasks, though it appears incremental as it builds on existing support-vector machine methods.
The paper tackles the problem of predicting object classifications with a modified support-vector machine that provides not only predictions but also a practical measure of evidence and confidence degrees for those predictions, presenting some experimental results.
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.