Abstract
This work tackles the problem of learning a robust classification function from a very small sample set when a related but unlabeled data set is provided. To this end we define a new semi-supervised method that is based on a stability criterion. We successfully apply our proposal in the specific case of automatic diagnosis of intestinal motility disease using video capsule endoscopy. An experimental evaluation shows the viability to apply the proposed method in motility disfunction diagnosis. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Seguí, S., Igual, L., Radeva, P., Malagelada, C., Azpiroz, F., & Vitrià, J. (2007). A semi-supervised learning method for motility disease diagnostic. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4756 LNCS, pp. 773–782). https://doi.org/10.1007/978-3-540-76725-1_80
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