A kernel-based optimum-path forest classifier
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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.