Instance-based learning
In machine learning, instance-based learning or 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. Instance-based learning is a kind of lazy learning.
It is called instance-based because it constructs hypotheses directly from the training instances themselves.[1] This means that the hypothesis complexity can grow with the data.[1]
A simple example of an instance-based learning algorithm is the k-nearest neighbour algorithm.
References
- ^ a b Russell, Stuart and Norvig, Peter: Artificial Intelligence: A Modern Approach, second edition, page 733. Prentice Hall, 2003. ISBN 0-13-080302-2
External link
- TiMBL, the Tilburg Memory Based Learner is an instance-based learning package geared toward natural language processing
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