Abstract
Graph-based approaches for pattern recognition techniques are commonly designed for unsupervised and semi-supervised ones. Recently, a novel collection of supervised pattern recognition techniques based on an optimum-path forest (OPF) computation in a feature space induced by graphs were presented: the OPF-based classifiers. They have some advantages with respect to the widely used supervised classifiers: they do not make assumption of shape/separability of the classes and run training phase faster. Actually, there exists two versions of OPF-based classifiers: OPF cpl (the first one) and OPF knn . Here, we introduce a learning algorithm for the last one and we show that a classifier can learns with its own errors without increasing its training set.
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Papa, J.P., Falcão, A.X. (2009). A Learning Algorithm for the Optimum-Path Forest Classifier. In: Torsello, A., Escolano, F., Brun, L. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2009. Lecture Notes in Computer Science, vol 5534. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02124-4_20
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DOI: https://doi.org/10.1007/978-3-642-02124-4_20
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