NDLI: Comparison on Confidence Bands of Decision Boundary between SVM and Logistic Regression

Content Provider	IEEE Xplore Digital Library
Author	Xing Wang Xin Wang Zhaonan Sun
Copyright Year	2009
Abstract	Support Vector Machine (SVM) and Logistic Regression (LR) are two popular classification models. The main purpose of a classification algorithm is to figure out the estimator for the decision boundary. In this paper, we considered confidence bands of decision boundary generated from SVM and LR. Confidence bands of decision boundary are estimated through bootstrap methods. We compared the confidence band estimator of SVM with the estimator of the conventional LR. Our main result is that sample size of the observations makes effect on the stability of both SVM and LR, sample size ratio, central location and covariance matrix of the data bring less effects on the stability of SVM than that of LR.
Starting Page	272
Ending Page	277
File Size	440682
Page Count	6
File Format	PDF
ISBN	9781424452095
DOI	10.1109/NCM.2009.281
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-25
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stability Logistic Regression Fasteners Classification algorithms Covariance matrix Support Vector Machine Statistics Support vector machines Support vector machine classification Statistical distributions Training data Bootstrap Confidence bands of decision boundary Logistics
Content Type	Text
Resource Type	Article

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Comparison on Confidence Bands of Decision Boundary between SVM and Logistic Regression