Paragraph Number

189

With reinforcement learning, input and output pairs can never be presented; reinforcement learning can map a state, e.g. a clinical state of a patient, and can develop predictions, actions or treatment the system can make. With representation learning algorithms input information can be preserved but transformed to make it more useful, e.g. as a pre-processing step, prior to performing classification or predictions, allowing reconstruction of the inputs coming from the unknown data generating distribution.

Added by DJM Jan 2024