An overview of the supervised machine learning methods

By Vladimir Nasteski. 2017

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Machine Learning in Aerodynamic Shape Optimization (2022, methodology): “Supervised Learning Supervised learning models are trained by data sets containing inputs and the corresponding model observations (also called “labels” or “targets”) [117, 118].”
Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions (2023, background): “The data are referred to be labelled because it consists of pairs, the desired output that can be deﬁned as a supervisory signal, and input that can be expressed as a vector [30,31].”
Evaluation of artificial intelligence techniques in disease diagnosis and prediction (2023, background): “The ML’s goal is that the machine can train itself based on input data set, experience, and receiving information from feedback [52].”