2010 TrainingandTestingLowDegreePoly

• (Chang et al., 2010) ⇒ Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang, Michael Ringgaard, and Chih-Jen Lin. (2010). “Training and Testing Low-degree Polynomial Data Mappings via Linear SVM.” In: The Journal of Machine Learning Research, 11.

Subject Headings: Low-Degree Polynomial Conjunctions.

Cited By

• http://scholar.google.com/scholar?q=%222010%22+Training+and+Testing+Low-degree+Polynomial+Data+Mappings+via+Linear+SVM
• http://dl.acm.org/citation.cfm?id=1756006.1859899&preflayout=flat#citedby

Quotes

Abstract

Kernel techniques have long been used in SVM to handle linearly inseparable problems by transforming data to a high dimensional space, but training and testing large data sets is often time consuming. In contrast, we can efficiently train and test much larger data sets using linear SVM without kernels. In this work, we apply fast linear-SVM methods to the explicit form of polynomially mapped data and investigate implementation issues. The approach enjoys fast training and time, but may sometimes achieve accuracy close to that of using highly nonlinear kernels. Empirical experiments show that the proposed method is useful for certain large-scale data sets. We successfully apply the proposed method to a natural language processing (NLP) application by improving the testing accuracy under some training / testing speed requirements.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 TrainingandTestingLowDegreePoly	Chih-Jen Lin Cho-Jui Hsieh Kai-Wei Chang Yin-Wen Chang Michael Ringgaard			Training and Testing Low-degree Polynomial Data Mappings via Linear SVM						2010