A Filter-SQP strategy for training Support Vector Machine models

• Autor(a) principal: Lino Bello, Tiago
• Data de Publicação: 2023
• Outros Autores: Matioli, Luiz Carlos, Pedroso, Lucas Garcia, Igarashi, Daniela Miray
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Remat (Bento Gonçalves)
• Texto Completo: https://periodicos.ifrs.edu.br/index.php/REMAT/article/view/6241
• topic: support vector machine; training; optimization; filter method; sequential quadratic programming; máquina de vetores de suporte; treinamento; otimização; método de filtro; programação quadrática sequencial; máquina de vectores de soporte; entrenamiento; optimización; método de filtro; programación cuadrática secuencial

This paper introduces a filtering strategy for addressing optimization problems arising in binary Support Vector Machine classification. The training optimization problem aims to solve the dual formulation which involves a quadratic objective function subjected to a linear and box constraints. Our approach employs a Filter algorithm with Sequential Quadratic Programming iterations that minimize the quadratic Lagrangian approximations. Notably, we utilize the exact Hessian matrix in our numerical experiments to seek the desired classification function. Moreover, we present a Filter algorithm combined with the Augmented Lagrangian method aiming to accelerate the algorithm convergence. To substantiate our method's effectiveness, we conduct numerical experiments through MATLAB, comparing outcomes with alternative methodologies detailed in existing literature. Numerical experiments shows that the Filter--SQP combined with Augmented Lagrangian method is competitive and efficient method compared with an interior-point based solver and LIBSVM software in relation of classification metrics and CPU-time.