2004 SupportVectorMachineLearningfor

• (Tsochantaridis et al., 2004) ⇒ Ioannis Tsochantaridis, Thomas Hofmann, Thorsten Joachims, and Yasemin Altun. (2004). “Support Vector Machine Learning for Interdependent and Structured Output Spaces.” In: Proceedings of the twenty-first International Conference on Machine learning. ISBN:1-58113-838-5 doi:10.1145/1015330.1015341

Subject Headings: Hierarchical SVM.

Cited By

• http://scholar.google.com/scholar?q=%222004%22+Support+Vector+Machine+Learning+for+Interdependent+and+Structured+Output+Spaces
• http://dl.acm.org/citation.cfm?id=1015330.1015341&preflayout=flat#citedby

Quotes

Abstract

Learning general functional dependencies is one of the main goals in machine learning. Recent progress in kernel-based methods has focused on designing flexible and powerful input representations. This paper addresses the complementary issue of problems involving complex outputs such as multiple dependent output variables and structured output spaces. We propose to generalize multiclass Support Vector Machine learning in a formulation that involves features extracted jointly from inputs and outputs. The resulting optimization problem is solved efficiently by a cutting plane algorithm that exploits the sparseness and structural decomposition of the problem. We demonstrate the versatility and effectiveness of our method on problems ranging from supervised grammar learning and named-entity recognition, to taxonomic text classification and sequence alignment.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2004 SupportVectorMachineLearningfor	Thorsten Joachims Yasemin Altun Thomas Hofmann Ioannis Tsochantaridis			Support Vector Machine Learning for Interdependent and Structured Output Spaces				10.1145/1015330.1015341		2004