2014 IncrementalandDecrementalTraini

• (Tsai et al., 2014) ⇒ Cheng-Hao Tsai, Chieh-Yen Lin, and Chih-Jen Lin. (2014). “Incremental and Decremental Training for Linear Classification.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623661

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Incremental+and+Decremental+Training+for+Linear+Classification
• http://dl.acm.org/citation.cfm?id=2623330.2623661&preflayout=flat#citedby

Quotes

Author Keywords

• Classifier design and evaluation; decremental learning; incremental learning; linear classification; warm start

Abstract

In classification, if a small number of instances is added or removed, incremental and decremental techniques can be applied to quickly update the model. However, the design of incremental and decremental algorithms involves many considerations. In this paper, we focus on linear classifiers including logistic regression and linear SVM because of their simplicity over kernel or other methods. By applying a warm start strategy, we investigate issues such as using primal or dual formulation, choosing optimization methods, and creating practical implementations. Through theoretical analysis and practical experiments, we conclude that a warm start setting on a high-order optimization method for primal formulations is more suitable than others for incremental and decremental learning of linear classification.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 IncrementalandDecrementalTraini	Chih-Jen Lin Cheng-Hao Tsai Chieh-Yen Lin			Incremental and Decremental Training for Linear Classification				10.1145/2623330.2623661		2014