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Recognition is achieved by the nearest neighbor classifier.
Recognition is achieved by linear discriminant analysis (LDA) and nearest neighbor classifier.
Particularly, a good performance of automatic speech recognition is achieved with use of speech recognition by statistical methods [17].
The structure damage pattern recognition is achieved through mimicking immune recognition mechanisms that possess features such as adaptation, evolution, and immune learning.
Recognition is achieved in two steps by calculating the similarity between the templates and motion features of the test and reference activities.
In the framework, features are captured transparently and hierarchically within a formal OWL ontology, and the feature recognition is achieved by applying an efficient backward-chained ontology reasoner to reason through the ontology.
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Some new recognition was achieved by comparing the results with previous study.
The same levels of texture recognition are achieved using these two interpolation methods as those using hardware-rotated textures.
In case of k-NN, the highest recognition was achieved for BFCC, 56.5% for acted speech and 74.5% for natural speech.
The value of the parameter λ was obtained by testing the algorithm for λ = 0.1, 0.2, …, 0.9 on an arbitrary set of free parameters and selecting the value for which the best recognition was achieved.
Recognition was achieved by the biotinylated 2F5 epitope peptide probe of secreted IgG "spots" from the 1D9 2F5-epitope-specific hybridoma cells for essentially all antibody secreting cells, validating probe specificity (Fig 5C).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com