2002 ExploitingRelationsAmongConcept

• (Bockhorst et al., 2002) ⇒ Joseph Bockhorst, and Mark Craven. (2002). “Exploiting Relations Among Concepts to Acquire Weakly Labeled Training Data.” In: Proceedings of the Nineteenth International Conference on Machine Learning (ICML 2002).

Subject Headings: Weakly Labeled Training Data.

Cited By

• http://scholar.google.com/scholar?q=%22Exploiting+Relations+Among+Concepts+to+Acquire+Weakly+Labeled+Training+Data%22+2002
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Quotes

Abstract

We consider a learning setting in which there are well defined relations that exist among instances of certain classes. In particular, we consider the domain of predicting various types of gene-regulation elements in bacterial genomes. Given instances of one class, we can often acquire "weakly labeled" training data from another class by taking advantage of known relationships that exist between two classes. The examples are weakly labeled in that either the class label is incompletely specified, the exact extent of an instance is only partially specified, or both. We use an EM-based approach to handle this hidden state during learning. Our experimental results show that, when only small training sets are available, there can be significant value in augmenting the training sets with weakly labeled examples acquired from relationships among concepts.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2002 ExploitingRelationsAmongConcept	Mark Craven Joseph Bockhorst			Exploiting Relations Among Concepts to Acquire Weakly Labeled Training Data