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The method learns the optimal correction strategy without the use of models or other hypotheses on the behavior of the physical chemical sensors.
Our proposed method learns the counting criteria after a short training stage, and uses the resulting classifier to process new images and obtaining both the number of transfected cells and the proportion of these cells that present a translocated protein.
Our method learns the spatio-temporal data structures and transitions that occur in the data without depending on manually predefined features to be searched for and works well in real time.
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.
The method learns the relevant set of parameters separately for each dataset, and generates site-specific inferences of the measure of interest, along with comparative statistics and error estimates.
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.
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However, the abovementioned methods learn the identification network in a holistic way, which ignores the structural information.
These existing part-based CNN methods learn the local features which are gradually ignored with the increase of neural network layers.
The PP method learns from the information-rich regions, and uses the learned knowledge to help the information-poor regions.
The method learns from the combination of two sets of data: phenotypic, objective and quantifiable variables, and expert assignments by qualified dermatologists.
By considering a Bayesian approach, the proposed method learns sequentially the nonlinear dynamics of moving agents over segments in which their orientations are relatively stable.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com