Busca avançada
Ano de início
Entree
(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

A new approach to contextual learning using interval arithmetic and its applications for land-use classification

Texto completo
Autor(es):
Pereira, Danillo Roberto ; Papa, Joao Paulo
Número total de Autores: 2
Tipo de documento: Artigo Científico
Fonte: PATTERN RECOGNITION LETTERS; v. 83, n. 2, p. 188-194, NOV 1 2016.
Citações Web of Science: 3
Resumo

Contextual-based classification has been paramount in the last years, since spatial and temporal information play an important role during the process of learning the behavior of the data. Sequential learning is also often employed in this context in order to augment the feature vector of a given sample with information about its neighborhood. However, most part of works describe the samples using features obtained through standard arithmetic tools, which may not reflect the data as a whole. In this work, we introduced the Interval Arithmetic to the context of land-use classification in satellite images by describing a given sample and its neighbors using interval of values, thus allowing a better representation of the model. Experiments over four satellite images using two distinct supervised classifiers showed we can considerably improve sequential learning-oriented pattern classification using concepts from Interval Arithmetic. (C) 2016 Elsevier B.V. All rights reserved. (AU)

Processo FAPESP: 14/16250-9 - Sobre a otimização de parâmetros em técnicas de aprendizado de máquina: avanços e paradigmas
Beneficiário:João Paulo Papa
Linha de fomento: Auxílio à Pesquisa - Regular
Processo FAPESP: 15/50319-9 - Meta-heuristic-based optimization of probabilistic neural networks
Beneficiário:João Paulo Papa
Linha de fomento: Auxílio à Pesquisa - Regular