Supervised pattern classification based on optimum‐path forest

By João Paulo Papa, Alexandre Xavier Falcão, C. T. N. Suzuki. International journal of imaging systems and technology (Print), 2009

354 citations recorded by the source corpus.

How researchers see this paper

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BBA: A Binary Bat Algorithm for Feature Selection (2012, methodology): “Each prototype sample deﬁnes its optimum-path tree (OPT), and the collection of all OPTs deﬁnes an optimum-path forest, which gives the name to the classiﬁer [13], [14].”
Segmentation (2019, methodology): “For arbitrary graphs, it becomes the Optimum-Path Forest framework [2, 16, 17, 19].”
FEMa: a finite element machine for fast learning (2019, methodology): “32 ; 32 (cid:4) , while the searching range of parameter c (variance of the Gaussian kernel) was deﬁned within the interval [0, 32].”
Clustering and Classification (2019, positive): “Classification based on optimum-path forest[1, 2, 3].”
Recent Advances in Open Set Recognition: A Survey (2018, methodology): “Moreover, the NNDR technique can be applied effortlessly to other classiﬁers based on the similarity score, e.g., the Optimum-Path Forest (OPF) classiﬁer [94].”