Loading…

Unsupervised and Semisupervised Classification Via Absolute Value Inequalities

We consider the problem of classifying completely or partially unlabeled data by using inequalities that contain absolute values of the data. This allows each data point to belong to either one of two classes by entering the inequality with a plus or minus value. By using such absolute value inequal...

Full description

Saved in:
Bibliographic Details
Published in:Journal of optimization theory and applications 2016-02, Vol.168 (2), p.551-558
Main Authors: Fung, Glenn M., Mangasarian, Olvi L.
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We consider the problem of classifying completely or partially unlabeled data by using inequalities that contain absolute values of the data. This allows each data point to belong to either one of two classes by entering the inequality with a plus or minus value. By using such absolute value inequalities in linear and nonlinear support vector machines, unlabeled or partially labeled data can be successfully partitioned into two classes that capture most of the correct labels dropped from the unlabeled data.
ISSN:0022-3239
1573-2878
DOI:10.1007/s10957-015-0818-5