Uma nova abordagem baseada em margem para seleção de modelos neurais

• Ano de defesa: 2012
• Autor(a) principal: Luiz Carlos Bambirra Torres
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; UFMG
• Palavras-chave em Português: Tomada de decisão; Grafo de gabriel; classificação; Aprendizado de máquina multiobjetivo; Processo decisório; Engenharia elétrica
• Link de acesso: http://hdl.handle.net/1843/BUBD-9HCFWJ

This work presents a new decision-making strategy to the multiobjective learning of artificial neural networks. The objective is to find the solution within the pareto-optimal set that has the best generalization performance. The proposed decision-making approach is based on a geometric approximation to the maximum margin (distance) of class separation, which is estimated through the following steps: modeling of input patterns using the gabriel graph, detection of class separation borders and synthesis of patterns along the maximum margin region. This methodology allows the selection of smooth (that ignore noise) and well-fitting models in a straightforward manner, i.e., without the need of the tuning of parameters by the user or the use of a representative validation data set. Results on benchmarks in literature showed that our decision-making method, combined with multiobjective training, was efficient to control the generalization of neural models.