A mixed quadratic programming model for a robust support vector machine

Raquel Serna Diaz; Raimundo Santos Leite; Paulo J. S. Silva

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A mixed quadratic programming model for a robust support vector machine

Serna-Diaz, Raquel ^[1] ; Santos Leite, Raimundo ^[2] ; S. Silva, Paulo J. ^[3]
1. [1] Universidad Nacional Agraria La Molina
  
  Universidad Nacional Agraria La Molina
  
  Perú
2. [2] Universidade Federal de Ouro Preto
  
  Universidade Federal de Ouro Preto
  
  Brasil
3. [3] Universidade Estadual de Campinas
  
  Universidade Estadual de Campinas
  
  Brasil
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Localización: Selecciones Matemáticas, ISSN-e 2411-1783, Vol. 8, Nº. 1, 2021 (Ejemplar dedicado a: Enero-Julio), págs. 27-36
Idioma: inglés
Títulos paralelos:
- Un modelo de programación cuadrática mixta para una máquina de soporte vectorial robusta
Enlaces
- Texto completo (pdf)
Resumen
- español
  Las máquinas de soporte vectorial son ampliamente usadas para resolver problemas de clasificación en el área de reconocimiento de patrones. Ellas tratan con pequeñas cantidades de errores utilizando el concepto de margen suave, dado que permite una clasificación imperfecta. Sin embargo, si los datos de entrenamiento tienen errores sistemáticos o valores atípicos, tal estrategia no es sólida resultando en una mala generalización. En este artículo presentamos un modelo robusto de clasificación de máquinas de soporte vectorial que hace posible ignorar aitomaticamente datos espurios Seguidamente mostramos que el modelo se puede resolver utilizando un software de alto rendimiento para programación cuadrática entera y se presentan experimentos numéricos que utilizan datos del mundo real que parecen prometedores.
- English
  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 allowfor 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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