Semi-supervised learning
This addresses classification tasks with limited labeled data, but it is incremental as it builds on existing semi-supervised learning methods with specific assumptions.
The paper tackles the problem of semi-supervised learning by proposing a new algorithm that, under necessary conditions, asymptotically achieves the performance of the best theoretical rule as unlabeled data size increases to infinity.
Semi-supervised learning deals with the problem of how, if possible, to take advantage of a huge amount of not classified data, to perform classification, in situations when, typically, the labelled data are few. Even though this is not always possible (it depends on how useful is to know the distribution of the unlabelled data in the inference of the labels), several algorithm have been proposed recently. A new algorithm is proposed, that under almost neccesary conditions, attains asymptotically the performance of the best theoretical rule, when the size of unlabeled data tends to infinity. The set of necessary assumptions, although reasonables, show that semi-parametric classification only works for very well conditioned problems.