|I am a first-year Ph.D. student at the Computer Science and Engineering Department of the University of California, San Diego.|
N. Trogkanis, G. Paliouras, "TPN2: Using positive-only learning to deal with the heterogeneity of labeled and unlabeled data", In Proceedings of the Discovery Challenge at the Joint European Conference on Machine Learning and on Principles and Practices of Knowledge Discovery in Databases (ECML/PKDD), pp. 63-74, Berlin, Germany, September, 2006. [manuscript]
This paper introduces TPN2, the runner up method in both tasks of the ECML-PKDD Discovery Challenge 2006 on personalized spam filtering. TPN2 is a classifier training method that bootstraps positive-only learning with fully-supervised learning, in order to make the most of labeled and unlabeled data, under the assumption that the two are drawn from significantly different distributions. Furthermore, the unlabeled data themselves are separated into subsets that are assumed to be drawn from multiple distributions. For that reason, TPN2 trains a different classifier for each subset, making use of all unlabeled data each time.
N. Trogkanis, "Learning methods of classifiers from positive examples with numeric attributes", National Technical University of Athens, 2006. [details+pdf]
My diploma thesis at NTUA. My advisor was Timos Sellis.
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Department of Computer Science & Engineering
University of California, San Diego
9500 Gilman Drive
La Jolla, CA 92093-0404
Last updated: December 20, 2006