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Supervised learning involves learning a mapping from inputs to outputs from "summary" of Machine Learning by Stephen Marsland
Supervised learning is a type of machine learning where the algorithm is trained on a labeled dataset. This means that each input in the dataset is paired with the correct output, allowing the algorithm to learn the relationship between inputs and outputs. The goal of supervised learning is to learn a mapping from inputs to outputs, which can then be used to predict the output for new, unseen inputs. In supervised learning, the algorithm learns from the labeled dataset by adjusting its internal parameters to minimize the difference between the predicted output and the true output. This is done through an iterative process where ...
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