Instance-based learning

In <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a>, instance-based learning (sometimes called memory-based learning) 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."
It is called instance-based because it constructs hypotheses directly from the training instances themselves.
This means that the hypothesis complexity can grow with the data: in the worst case, a hypothesis is a list of n training items and the computational complexity of <a href="/facts/Classification_(machine_learning)/jXXHRkXR">classifying</a> a single new instance is <a href="/facts/Big_O_notation/weFFjSWg">O</a>(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 <a href="/facts/K-nearest_neighbors_algorithm/Bs1UaAmE">k-nearest neighbors algorithm</a>, <a href="/facts/Kernel_method/fYuURIPk">kernel machines</a> and <a href="/facts/Radial_basis_function_network/KBesEWD3">RBF networks</a>.: 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 <a href="/facts/Overfitting/5xnFLcMg">overfitting</a> to noise in the training set, instance reduction algorithms have been proposed.

Instance-based learning open-in-new

Instance-based learning