Abstract
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 this article, transductive learning machines are introduced based on a so-called affinity rule that if two objects are close in input space then their outputs should also be close, to obtain the solution of semi-supervised learning problems. The analytic solution for the problem and its iterated algorithm are obtained. Some simulations about pattern classification are conducted to demonstrate the validity of the proposed method in different situations. An incremental learning algorithm adapting to on-line data processing is also derived.
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Long, W., Zhang, W. (2004). Transductive Learning Machine Based on the Affinity-Rule for Semi-supervised Problems and Its Algorithm. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_42
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DOI: https://doi.org/10.1007/978-3-540-28647-9_42
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