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Speeding up optimum-path forest training by path-cost propagation
(2012-12-01)
In this paper we present an optimization of the Optimum-Path Forest classifier training procedure, which is based on a theoretical relationship between minimum spanning forest and optimum-path forest for a specific path-cost ...
Speeding up optimum-path forest training by path-cost propagation
(2012-12-01)
In this paper we present an optimization of the Optimum-Path Forest classifier training procedure, which is based on a theoretical relationship between minimum spanning forest and optimum-path forest for a specific path-cost ...
Hyperspectral Data Classification Improved By Minimum Spanning Forests
(Spie-Soc Photo-Optical Instrumentation EngineersBellingham, 2016)
Hyperspectral Data Classification Improved By Minimum Spanning Forests
(SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERSBELLINGHAM, 2016)
Efficient Forest Data Structure for Evolutionary Algorithms Applied to Network Design
(IEEE-Inst Electrical Electronics Engineers IncPiscataway, 2012)
Aprendizado incremental e classe-incremental por meio da atualização de árvores geradoras em florestas de caminhos ótimos
(Sociedade Brasileira de Computação - SBCUniversidade Federal de São Paulo - UNIFESPInstituto Nacional de Pesquisas Espaciais - INPESão José dos Campos, 2016-10)
Algoritmos com capacidade classe-incremental, em que é preciso manter modelos de classificação atualizados a partir de dados que aparecem ao longo do tempo, são importantes em diversas aplicações. Apresentamos neste artigo ...
Efficient Forest Data Structure for Evolutionary Algorithms Applied to Network Design
(IEEELos Alamitos, 2012)
The design of a network is a solution to several engineering and science problems. Several network design problems are known to be NP-hard, and population-based metaheuristics like evolutionary algorithms (EAs) have been ...
A path- and label-cost propagation approach to speedup the training of the optimum-path forest classifier
(Elsevier B.V., 2014-04-15)
In general, pattern recognition techniques require a high computational burden for learning the discriminating functions that are responsible to separate samples from distinct classes. As such, there are several studies ...