Minimizing the error of linear separators on linearly inseparable data

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• 作者：Boris Aronov^a ; ^{aronov@poly.edu} ; Delia Garijo^b ; ^{dgarijo@us.es} ; Yurai Nú ; ñ ; ez-Rodrí ; guez^c ; ^{yurai@cs.queensu.ca} ; David Rappaport^d ; ^{daver@cs.queensu.ca} ; Carlos Seara^e ; ^{carlos.seara@upc.edu} ; Jorge Urrutia^f ; ^{urrutia@matem.unam.mx}
• 关键词：Linearly inseparable ; Classifiers ; Error minimizers
• 刊名：Discrete Applied Mathematics
• 出版年：2012
• 期刊代码：46_0166218x
• 类别：cp
• 出版时间：July, 2012
• 卷：160
• 期：10-11
• 页码：1441-1452
• 文件大小：301 K

摘要

Given linearly inseparable sets of red points and of blue points, we consider several measures of how far they are from being separable. Intuitively, given a potential separator (鈥渃lassifier鈥?, we measure its quality (鈥渆rror鈥? according to how much work it would take to move the misclassified points across the classifier to yield separated sets. We consider several measures of work and provide algorithms to find linear classifiers that minimize the error under these different measures.