2015 WarmStartforParameterSelectiono

• (Chu et al., 2015) ⇒ Bo-Yu Chu, Chia-Hua Ho, Cheng-Hao Tsai, Chieh-Yen Lin, and Chih-Jen Lin. (2015). “Warm Start for Parameter Selection of Linear Classifiers.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783332

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Warm+Start+for+Parameter+Selection+of+Linear+Classifiers
• http://dl.acm.org/citation.cfm?id=2783258.2783332&preflayout=flat#citedby

Quotes

Author Keywords

• General; linear classification; regularization parameter; warm start

Abstract

In linear classification, a regularization term effectively remedies the overfitting problem, but selecting a good regularization parameter is usually time consuming. We consider cross validation for the selection process, so several optimization problems under different parameters must be solved. Our aim is to devise effective warm-start strategies to efficiently solve this sequence of optimization problems. We detailedly investigate the relationship between optimal solutions of logistic regression / linear SVM and regularization parameters. Based on the analysis, we develop an efficient tool to automatically find a suitable parameter for users with no related background knowledge.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 WarmStartforParameterSelectiono	Chih-Jen Lin Chia-Hua Ho Cheng-Hao Tsai Chieh-Yen Lin Bo-Yu Chu			Warm Start for Parameter Selection of Linear Classifiers				10.1145/2783258.2783332		2015