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The aim of LAF is to classify biological sequences and assign them to their taxonomic unit with the aid of a supervised machine learning paradigm [ 51] (see subsection Supervised machine learning and rule-based classification algorithms for more details).
Fig. 1 Supervised machine learning workflow.
Features can be combined through a supervised machine learning approach.
In contrast, our approach is based on supervised machine learning.
SVMs is a supervised machine learning method which can be used for classification or regression analysis.
In supervised machine learning, the identification of these diagnostic classification terms is known as feature extraction.
There are multiple methods for measuring sentiments, including lexical-based and supervised machine learning methods.
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The inference of the model is conceived as a supervised machine-learning task.
Semi-supervised machine learning methods have the features of both, integrating labeled and unlabeled training data.
Semi-supervised machine learning of preM ER+ patients revealed three groups with strikingly different outcomes.
Here, we applied a three-stage semi-supervised machine learning algorithm to estimate gene essentiality during erythroid precursor maturation.
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