Transductive Confidence Machines for Pattern Recognition
We propose a new algorithm for pattern recognition that outputs some measures of "reliability" for every prediction made, in contrast to the current algorithms that output "bare" predictions only. Our method uses a rule similar to that of nearest neighbours to infer predictions; thus its predictive performance is close to that of nearest neighbours, while the measures of confidence it outputs provide practically useful information for individual predictions.