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The longitudinal features from each patient's eye were then fed to the machine learning classifier to classify each eye as stable or progressed over time.
Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other).
And like I said, I'm using a simple linear machine learning classifier.
Next, we'll understand the machine learning classifier we'll be using for this task.
A machine learning classifier, with good feature templates for text categorization.
Furthermore, the machine learning classifier performed well on free-text knee MRI reports from another institution.
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I put it into a linear machine learning-classifier, and I then, for a new test point, will get out a predicted label.
In order to train any supervised machine-learning classifier it is required to have a gold standard training set of classified examples.
As already mentioned, the support vector machine (SVM) is a machine-learning classifier that permits to find an optimal separating hyper-plane.
Then, we trained a machine-learning classifier (a Support Vector Machine, SVM) to automatically predict coma outcome (good poor) based on whole-brain FC of comatose patients.
NFI is a machine-learning classifier based on a linear support vector machine, not a parameter to measure disease severity.
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