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• W1976445448 abstract "In this article, the task of remote-sensing image classification is tackled with local maximal margin approaches. First, we introduce a set of local kernel-based classifiers that alleviate the computational limitations of local support vector machines (SVMs), maintaining at the same time high classification accuracies. Such methods rely on the following idea: (a) during training, build a set of local models covering the considered data and (b) during prediction, choose the most appropriate local model for each sample to evaluate. Additionally, we present a family of operators on kernels aiming to integrate the local information into existing (input) kernels in order to obtain a quasi-local (QL) kernel. To compare the performances achieved by the different local approaches, an experimental analysis was conducted on three distinct remote-sensing data sets. The obtained results show that interesting performances can be achieved in terms of both classification accuracy and computational cost." @default.
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• W1976445448 date "2012-04-20" @default.
• W1976445448 modified "2023-09-27" @default.
• W1976445448 title "Local SVM approaches for fast and accurate classification of remote-sensing images" @default.
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• W1976445448 doi "https://doi.org/10.1080/01431161.2012.678947" @default.
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