@AudiovisualMaterial{PapaFalMirSuzMas:2007:DeRoPa,
abstract = "We present a supervised pattern classifier based on optimum path
forest. The samples in a training set are nodes of a complete
graph, whose arcs are weighted by the distances between sample
feature vectors. The training builds a classifier from key samples
(prototypes) of all classes, where each prototype defines an
optimum path tree whose nodes are its strongest connected samples.
The optimum paths are also considered to label unseen test samples
with the classes of their strongest connected prototypes. We show
how to find prototypes with none classification errors in the
training set and propose a learning algorithm to improve accuracy
over an evaluation set. The method is robust to outliers, handles
non-separable classes, and can outperform support vector
machines.",
author = "Papa, Jo{\~a}o Paulo and Falc{\~a}o, Alexandre X. and Miranda,
Paulo A. V. and Suzuki, Celso T. N. and Mascarenhas, Nelson D.
A.",
city = "Rio de Janeiro",
conferencename = "International Symposium on Mathematical Morphology, 8 (ISMM).",
date = "Oct. 2007",
keywords = "supervised classifiers, image foresting transform, image analysis,
pattern recognition.",
language = "en",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
publisheraddress = "S{\~a}o Jos{\'e} dos Campos",
ibi = "83LX3pFwXQZ3qyBY/RL3kk",
url = "http://urlib.net/ibi/83LX3pFwXQZ3qyBY/RL3kk",
targetfile = "papa_opf.pdf",
title = "Design of robust pattern classifiers based on optimum-path
forests",
type = "Watershed segmentation",
year = "2007",
urlaccessdate = "08 maio 2024"
}