Locally weighted learning

Locally weighted learning or lazy learning refers to remembering all the training data for a classification or value-prediction problem and only generating a model as and when new data is to be processed. This allows more relevant training data to be weighted more strongly than less relevant training data. The weightings are most frequently associated with the Euclidian distance between the new data point and each individual training point.