2008 ClassificationwithPartialLabels

• (Nguyen et al., 2008) ⇒ Nam Nguyen, and Rich Caruana. (2008). “Classification with Partial Labels.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401958

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• http://scholar.google.com/scholar?q=%22Classification+with+partial+labels%22+2008
• http://portal.acm.org/citation.cfm?doid=1401890.1401958&preflayout=flat#citedby

Quotes

Abstract

In this paper, we address the problem of learning when some cases are fully labeled while other cases are only partially labeled, in the form of partial labels. Partial labels are represented as a set of possible labels for each training example, one of which is the correct label. We introduce a discriminative learning approach that incorporates partial label information into the conventional margin-based learning framework. The partial label learning problem is formulated as a convex quadratic optimization minimizing the L2-norm regularized empirical risk using hinge loss. We also present an efficient algorithm for classification in the presence of partial labels. Experiments with different data sets show that partial label information improves the performance of classification when there is traditional fully-labeled data, and also yields reasonable performance in the absence of any fully labeled data.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 ClassificationwithPartialLabels	Rich Caruana Nam Nguyen			Classification with Partial Labels		KDD-2008 Proceedings		10.1145/1401890.1401958		2008