It is called instance-based because it constructs hypotheses directly from the training instances themselves. 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 algorithm are the k-nearest neighbor algorithm, kernel machine learning tom mitchell mcgraw hill pdf and RBF networks.
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. Gagliardi applies this family of classifiers in medical field as second-opinion diagnostic tools and as tools for the knowledge extraction phase in the process of knowledge discovery in databases. Artificial Intelligence: A Modern Approach, second edition, p. Reduction techniques for instance-based learning algorithms”.
Instance-based classifiers applied to medical databases: Diagnosis and knowledge extraction”. This artificial intelligence-related article is a stub. You can help Wikipedia by expanding it.
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