A kernel-based optimum-path forest classifier

• Autor(a) principal: Afonso, Luis C. S.
• Data de Publicação: 2018
• Outros Autores: Pereira, Danillo R., Papa, João P. [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1007/978-3-319-75193-1_78; http://hdl.handle.net/11449/179600
• topic: Kernel; Optimum-path forest; Support vector machines

The modeling of real-world problems as graphs along with the problem of non-linear distributions comes up with the idea of applying kernel functions in feature spaces. Roughly speaking, the idea is to seek for well-behaved samples in higher dimensional spaces, where the assumption of linearly separable samples is stronger. In this matter, this paper proposes a kernel-based Optimum-Path Forest (OPF) classifier by incorporating kernel functions in both training and classification steps. The proposed technique was evaluated over a benchmark comprised of 11 datasets, whose results outperformed the well-known Support Vector Machines and the standard OPF classifier for some situations.