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. 2022 Jul 12;18:517–528. doi: 10.2147/VHRM.S279337

Table 1.

Machine Learning (ML) Techniques Which Enable Artificial Intelligence (AI)

Type of Learning Machine Learning Algorithm Outcomes Strengths Weaknesses
Supervised
Labeled data with defined outputs
Classification Categorical Training data set is reusable if features do not change Large, accurately labelled training data sets are required. Can be costly and time-consuming
At risk of overfitting and does not generalize well if the training data set is heterogenous
Regression Continuous
Unsupervised
Unlabeled data with unknown outputs
Clustering Similarity of Inputs No previous knowledge of the data set is required and hence the scope of human error is reduced. Faster to perform. The spectral classes do not necessarily represent features on the ground and can take time to interpret
Dimensionality Reduction Extract Relevant Features
Association Co-occurrence Likelihood
Anomaly Reduction Outliers
Semi-Supervised Generative Combination of supervised and unsupervised outcomes Stable algorithm which reduces the time needed to annotate date Iteration results are not stable and can hence have a low accuracy
Reinforcement Reward based Sequential decision making Can self-correct inherent errors introduced during programming Requires a lot of data and computational power