Instance Based Learning
ABOUT
Instance-based learning methods comprise learning algorithms which store instances seen in training. Instead of processing and generalising seen instances immediatly, instance-based learning algorithms delay processing until a new instance must be classified, the reason why they are also called lazy learning. In a decision making process, instance-based learning algorithms directly compare upcoming instances to instances stored in memory. The main advantage of instance-based learning algorithms is its simplicity. The most basic instance-based learning method is the k-Nearest Neighbour algorithm, but Locally Weighted Regression and Radial Basis Function are also common methods.
RESOURCES
Walter Daelemans; Antal van den Bosch (2005). Memory-Based Language Processing. Cambridge University Press.
Stuart Russell and Peter Norvig (2003). Artificial Intelligence: A Modern Approach, second edition, p. 733. Prentice Hall.
Aha, D. W., Kibler, D., & Albert, M. K. (1991). Instance-based learning algorithms. Machine learning, 6(1), 37-66.
