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The proposed approach has been tested on synthetic images, and then has applied to an urban environment for the classification of data in Quickbird images.
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A large-scale machine learning classifier based on functional networks was used in [54] for the classification of biomedical data.
In this work, we present a novel boosting algorithm for the classification of imbalanced data.
In this section, we compare the Raw kernel to linear kernel for the classification of CGH data.
In Bioinformatics, l1LS sparse coding has been applied for the classification of microarray gene expression data in [ 11].
In [47], several neural networks were compared to the SVM for the classification of hyper-spectral data.
Data mining techniques have been used for the classification of many types of products.
Regarding to this, a new method is presented for classification of data of a medical database in this study.
By inspiration of the granular evolutionary algorithm, a Granular Agent Evolutionary Classification (GAEC) algorithm for the classification task in data mining is proposed.
Regularized least square algorithm is used for classification of data.
In this section, we present an algorithm for the supervised classification of arrayCGH data.
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