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We extracted the news titles from the Guardian web site for our classification purpose.
We collected sound clips from several sources in order to create ground truth databases for our classification and detection experiments.
This section introduces the Ambit Biosciences' dataset [7] that provides us with class information for our classification task.
We used multiple nets and image transformations to optimize accuracy for our classification task, achieving a surprisingly low error rate of just 0.072%.
As discussed, we were able to use liberal or stringent criteria for our classification of detection and location accuracy on the manipulated image trials.
In other words, instead of the original training data X l, we use the set of activations A l = { a i l } as feature vectors for our classification task.
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The synteny-based classification scheme conflicts with the available experimental data [40] specifically for those proteins for which it differs from our classification.
This was helpful to evaluate the performance of our classification approach for more obviously implicated genes and loci.
For example, our classification tree for BLCA highlights alterations affecting RB1 and ARID1A in CIMP+ tumors (Additional file 1: Figure S3), consistent with previous independent analyses [ 19, 41].
Furthermore, there are some caveats for reading our classification.
The spectroscopic data measured on hypertrophic and non-hypertrophic scar tissues were used for developing our classification algorithm.
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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