Reinforcement Learning

Arya MirAI and Robotics

Nov 26, 2011 (4 years and 11 months ago)


Machine learning is traditionally divided into: Supervised learning . . . takes a set of pre-labelled examples, i.e., input-output pairs. extracts a mapping allowing us to predict a label (classification) or a function value (regression) corresponding to an ‘unseen’ input. Examples: decision tree (ID3), perceptron algorithm, support vector machine. Unsupervised learning . . . As for supervised learning, but the example data are unlabelled, i.e., no outputs. Typically works by clustering

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