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 |