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Issue No.01 - January (2008 vol.30)
pp: 174-179
ABSTRACT
Graph-based learning provides a useful approach for modeling data in classification problems. In this modeling scenario, the relationship between labeled and unlabeled data impacts the construction and performance of classifiers, and therefore a semi-supervised learning framework is adopted. We propose a graph classifier based on kernel smoothing. A regularization framework is also introduced, and it is shown that the proposed classifier optimizes certain loss functions. Its performance is assessed on several synthetic and real benchmark data sets with good results, especially in settings where only a small fraction of the data are labeled.
INDEX TERMS
Machine learning, Nonparametric statistics, Statistical methods
CITATION
Mark Culp, George Michailidis, "Graph-Based Semisupervised Learning", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.30, no. 1, pp. 174-179, January 2008, doi:10.1109/TPAMI.2007.70765
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