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Support vector machine classifier is used for text classification in various studies.
In the classification stage, a support vector machine classifier is employed.
Support vector machine classifier is deployed in leave-one-out cross-validation manner to carry out classification process.
Details regarding the design and implementation of a decision support tool making use of the developed support vector machine classifier is then elaborated on in a separate section.
After the features are extracted, a multi-class non-linear support vector machine classifier is used to implement protein structural classes prediction.
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A multi-class Support Vector Machine classifier was also trained using all four metrics and produced better classification results than the simpler joint classifier, at the cost of requiring a larger training dataset.
A series of appropriate methods are attempted to tackle several challenges in the framework, and the Naïve Bayes classifier and the Support Vector Machine classifier are employed for TCM automatic diagnosis.
An SVM (support vector machine) classifier was constructed for TFMRA, TFMRA+SLR, and the original 85 signature genes.
A support vectors machine classifier was able to distinguish between dose-rates with 100%% accuracy using leave-one-out cross-validation.
A support vector machine classifier was first trained on expert-defined small vessel ischemic disease (SVID) lesions in 45 ACCORD-MIND cases and then was used to classify SVID in new cases.
A support vector machine classifier was first trained on expert-defined small-vessel ischemic disease (SVID) lesions in 45 cases from the Action to Control Cardiovascular Risk in Diabetes Memory in Diabetes (ACCORD-MIND) study (14) and then used to classify SVID in new scans.
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