2008 ASequentialDualMethodforLargeSc

• (Keerthi et al., 2008) ⇒ S. Sathiya Keerthi, S. Sundararajan, Kai-Wei Chang, Cho-Jui Hsieh, and Chih-Jen Lin. (2008). “A Sequential Dual Method for Large Scale Multi-class Linear Svms.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401942

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%22A+sequential+dual+method+for+large+scale+multi-class+linear+svms%22+2008
• http://portal.acm.org/citation.cfm?doid=1401890.1401942&preflayout=flat#citedby

Quotes

Abstract

Efficient training of direct multi-class formulations of linear Support Vector Machines is very useful in applications such as text classification with a huge number examples as well as features. This paper presents a fast dual method for this training. The main idea is to sequentially traverse through the training set and optimize the dual variables associated with one example at a time. The speed of training is enhanced further by shrinking and cooling heuristics. Experiments indicate that our method is much faster than state of the art solvers such as bundle, cutting plane and exponentiated gradient methods.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 ASequentialDualMethodforLargeSc	Chih-Jen Lin S. Sathiya Keerthi Cho-Jui Hsieh Kai-Wei Chang S. Sundararajan			A Sequential Dual Method for Large Scale Multi-class Linear Svms				10.1145/1401890.1401942