Uma estratégia Filtro-SQP para treinamento de modelos de Máquina de Vetores de Suporte
DOI:
https://doi.org/10.35819/remat2023v9i2id6241Palavras-chave:
máquina de vetores de suporte, treinamento, otimização, método de filtro, programação quadrática sequencialResumo
Neste artigo, introduzimos uma estratégia de Filtro para resolver o problema de otimização decorrente da do método de classificação Máquina de Vetores de Suporte. Este problema de treinamento visa resolver a formulação dual, que envolve uma função objetiva quadrática sujeita a uma restrição lineares de igualdade e caixa. Esta abordagem aplica um algoritmo de Filtro com iterações de Programação Quadrática Sequencial, que minimizam as aproximações Lagrangiano quadráticas, usando a matriz Hessiana exata nos experimentos numéricos, em busca da função de classificação desejada. Apresenta-se um algoritmo de Filtro combinado com o método do Lagrangiano Aumentado visando acelerar a convergência do algoritmo. Também apresentamos resultados numéricos obtidos ao implementar nosso algoritmo proposto no MATLAB e comparar os resultados com outras metodologias da literatura. Os experimentos numéricos mostram que o método de Filtro-SQP combinado com o método do Lagrangiano Aumentado é um método competitivo e eficiente em relação às métricas de classificação e tempo de CPU, em comparação com um solucionador baseado em Pontos Interiores e o software LIBSVM.
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