Una estrategia de Filtro-SQP para entrenar modelos de Máquinas de Vectores de Soporte
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https://doi.org/10.35819/remat2023v9i2id6241Palabras clave:
máquina de vectores de soporte, entrenamiento, optimización, método de filtro, programación cuadrática secuencialResumen
En este trabajo, exponermos una estrategia de Filtro para resolver problemas de optimización que surgen en la clasificación binaria de Máquinas de Vectores de Soporte. El problema de optimización del entrenamiento tiene como objetivo resolver la formulación dual que implica una función objetiva cuadrática sujeta a restricciones lineales y de caja. Nuestro enfoque aplica un algoritmo de Filtro con iteraciones de Programación Cuadrática Secuencial que minimizan las aproximaciones Lagrangianas cuadráticas, utilizando la matriz Hessiana exacta en los experimentos numéricos, en busca de la función de clasificación deseada. Presentamos un algoritmo de Filtro combinado con el método del Lagrangiano Aumentado con el objetivo de acelerar la convergencia del algoritmo. También presentamos resultados numéricos obtenidos al implementar nuestro algoritmo propuesto en MATLAB, y comparamos los resultados con otras metodologías en la literatura. Los experimentos numéricos muestran que el método del Filtro-SQP combinado con el método del Lagrangiano Aumentado es un método competitivo y eficiente en comparación con un solucionador basado en puntos interiores y el software LIBSVM en términos de métricas de clasificación y tiempo de CPU.
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