Unsupervised classification via convex absolute value inequalities
| Autor: | Olvi L. Mangasarian |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Control and Optimization
Inequality business.industry Unlabelled data Applied Mathematics media_common.quotation_subject Regular polygon Absolute value Pattern recognition Management Science and Operations Research Support vector machine Artificial intelligence business Classifier (UML) media_common Mathematics |
| Zdroj: | Optimization. 64:81-86 |
| ISSN: | 1029-4945 0233-1934 |
| DOI: | 10.1080/02331934.2014.947501 |
| Popis: | We consider the problem of classifying completely unlabelled data using convex 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. Using such absolute value inequalities in support vector machine classifiers, unlabelled data can be successfully partitioned into two classes that capture most of the correct labels dropped from the data. Inclusion of partially labelled data leads to a semisupervised classifier. Computational results include unsupervised and semisupervised classification of the Wisconsin Breast Cancer Wisconsin (Diagnostic) Data Set. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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