An Incremental Algorithm about the Affinity-Rule Based Transductive Learning Machine for Semi-Supervised Problem
Wei-Jiang Long, Feng-feng Zhu, Wen‐Xiu Zhang · 2006
One of the central problems in machine learning is how to effectively combine unlabelled and labelled data to infer the labels of unlabelled ones. In recent years, there has a growing interest on the transduction method. In this article, the transductive learning machines are described based on a so-called affinity rule which comes from the intuitive fact that if two objects are close in input space then their outputs should also be close, to obtain the solution of semi-supervised learning problem. By using the analytic solution for this problem, an incremental learning algorithm adapting to on-line data processing is derived.