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The proposed GESR-LR method learns a low-rank weight matrix by projecting the data onto a low-dimensional subspace.
Unlike most existing methods which utilize hand-crafted descriptors for face representation, our GA-DFL method learns a discriminative feature descriptor per image directly from raw pixels for face representation under the deep convolutional neural networks framework.
The JNMF method learns a common base matrix from different sources that best approximates the original sources.
Using a subset of the orthologs between two species and a small training set, their method learns a new distance function between microarray experiments in the two species.
In the training phase, the method learns a collection of gene-gene interaction effects from compendium of microarray experiments (training compendium), which are captured in an interaction network.
Specifically, our method learns a Mahalanobis metric for a set of genomes and a reference taxonomy to guide the learning process.
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Changing location means recoding the method; learning a new vernacular of murder.
Most deep learning-based image SR methods learn an end-to-end network to discover the mapping relationship between low-resolution (LR) and high-resolution (HR) images in order to produce visually satisfactory images.
SVM classification method learns from a training dataset and attempts to generalize and make correct predictions on novel data.
A supervised method "learns" the distinctive features of a given biological function from a training set of genes, in which some of them are known to have the function of interest and others are supposed not to have it.
Meinshausen's method learns the network under a single condition, and we combined the results learned under each condition to get conserved network and differential network.
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