This paper presents a novel graph-based algorithm for solv- ing the semi-supervised learning problem. The graph-based algorithm makes use of the recent advances in stochastic graph sampling technqiue and a modeling of the labeling consistency in semi-supervised learning. The quality of the algorithm is empirically evaluated on a synthetic clus- tering problem. The semi-supervised clustering is also applied to the problem of symptoms classification in medical image database and shows promising results.
CITATION STYLE
Li, C. H., & Yuen, P. C. (2001). Semi-supervised learning in medical image database. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 2035, pp. 154–160). Springer Verlag. https://doi.org/10.1007/3-540-45357-1_19
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