2012 LinearSupportVectorMachinesviaD

• (Matsushima et al., 2012) ⇒ Shin Matsushima, S.V.N. Vishwanathan, and Alexander J. Smola. (2012). “Linear Support Vector Machines via Dual Cached Loops.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339559

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+Linear+Support+Vector+Machines+via+Dual+Cached+Loops
• http://dl.acm.org/citation.cfm?id=2339530.2339559&preflayout=flat#citedby

Quotes

Author Keywords

• Classifier design and evaluation; coordinate descent; optimization; support vector machines

Abstract

Modern computer hardware offers an elaborate hierarchy of storage subsystems with different speeds, capacities, and costs associated with them. Furthermore, processors are now inherently parallel offering the execution of several diverse threads simultaneously. This paper proposes StreamSVM, the first algorithm for training linear Support Vector Machines (SVMs) which takes advantage of these properties by integrating caching with optimization. StreamSVM works by performing updates in the dual, thus obviating the need to rebalance frequently visited examples. Furthermore we trade off file I/O with data expansion on the fly by generating features on demand. This significantly increases throughput. Experiments show that StreamSVM outperforms other linear SVM solvers, including the award winning work of [38], by orders of magnitude and produces more accurate solutions within a shorter amount of time.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 LinearSupportVectorMachinesviaD	Alexander J. Smola Shin Matsushima S.V.N. Vishwanathan			Linear Support Vector Machines via Dual Cached Loops				10.1145/2339530.2339559		2012