Minimizing the error of linear separators on linearly inseparable data

Boris Aronov, Delia Garijo, Yurai Nez-Rodrguez, David Rappaport, Carlos Seara, Jorge Urrutia

    Research output: Contribution to journalArticle


    Given linearly inseparable sets R of red points and B of blue points, we consider several measures of how far they are from being separable. Intuitively, given a potential separator ("classifier"), we measure its quality ("error") 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.

    Original languageEnglish (US)
    Pages (from-to)1441-1452
    Number of pages12
    JournalDiscrete Applied Mathematics
    Issue number10-11
    StatePublished - Jul 1 2012



    • Classifiers
    • Error minimizers
    • Linearly inseparable

    ASJC Scopus subject areas

    • Discrete Mathematics and Combinatorics
    • Applied Mathematics

    Cite this

    Aronov, B., Garijo, D., Nez-Rodrguez, Y., Rappaport, D., Seara, C., & Urrutia, J. (2012). Minimizing the error of linear separators on linearly inseparable data. Discrete Applied Mathematics, 160(10-11), 1441-1452.