Definition
Instance-based learning refers to a family of techniques for classification and regression, which produce a class label/predication based on the similarity of the query to its nearest neighbor(s) in the training set. In explicit contrast to other methods such as decision trees and neural networks, instance-based learning algorithms do not create an abstraction from specific instances. Rather, they simply store all the data, and at query time derive an answer from an examination of the query’s nearest neighbor(s).
Somewhat more generally, instance-based learning can refer to a class of procedures for solving new problems based on the solutions of similar past problems.
Motivation and Background
Most instance-based learning algorithms can be specified by determining the following four items:
- 1.
Distance measure: Since the notion of similarity is being used to...
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Recommended Reading
Aha, D. W., Kibler, D., & Albert, M. K. (1991). Instance-based learning algorithms. Machine Learning, 6, 37–66.
Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., & Keogh, E. J. (2008). Querying and mining of time series data: Experimental comparison of representations and distance measures. PVLDB, 1(2), 1542–1552.
Wilson, D. R., & Martinez, T. R. (2000). Reduction techniques for exemplar-based learning algorithms. Machine Learning, 38(3), 257–286.
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Keogh, E. (2011). Instance-Based Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_409
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DOI: https://doi.org/10.1007/978-0-387-30164-8_409
Publisher Name: Springer, Boston, MA
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