Instance-based learning

In machine learning, instance-based learning (sometimes called memory-based learning^[1]) is a family of learning algorithms that, instead of performing explicit generalization, compare new problem instances with instances seen in training, which have been stored in memory. Because computation is postponed until a new instance is observed, these algorithms are sometimes referred to as "lazy."^[2]

It is called instance-based because it constructs hypotheses directly from the training instances themselves.^[3] This means that the hypothesis complexity can grow with the data:^[3] in the worst case, a hypothesis is a list of n training items and the computational complexity of classifying a single new instance is O(n). One advantage that instance-based learning has over other methods of machine learning is its ability to adapt its model to previously unseen data. Instance-based learners may simply store a new instance or throw an old instance away.

Examples of instance-based learning algorithms are the k-nearest neighbors algorithm, kernel machines and RBF networks.^{[2]: ch. 8} These store (a subset of) their training set; when predicting a value/class for a new instance, they compute distances or similarities between this instance and the training instances to make a decision.

To battle the memory complexity of storing all training instances, as well as the risk of overfitting to noise in the training set, instance reduction algorithms have been proposed.^[4]