A mixed quadratic programming model for a robust support vector machine.
Support Vector Machines are extensively used to solve classification problems in Pattern Recognition. They deal with small errors in the training data using the concept of soft margin, that allow for imperfect classification. However, if the training data have systematic errors or outliers such stra...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | Brasil |
| Institución: | Universidade Federal de Ouro Preto (UFOP) |
| Repositorio: | Repositório Institucional da UFOP |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.ufop.br:123456789/16117 |
| Acceso en línea: | http://www.repositorio.ufop.br/jspui/handle/123456789/16117 https://dx.doi.org/10.17268/sel.mat.2021.01.03 |
| Access Level: | acceso abierto |
| Palabra clave: | Mixed integer quadratic programming Outliers Classification |
| Sumario: | Support Vector Machines are extensively used to solve classification problems in Pattern Recognition. They deal with small errors in the training data using the concept of soft margin, that allow for imperfect classification. However, if the training data have systematic errors or outliers such strategy is not robust resulting in bad generalization. In this paper we present a model for robust Support Vector Machine classification that can automatically ignore spurius data. We show then that the model can be solved using a high performance Mixed Integer Quadratic Programming solver and present preliminary numerical experiments using real world data that looks promissing. |
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