Instance-based learning: Difference between revisions

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A simple example of an instance-based learning algorithm is the [[k-nearest neighbor algorithm]]. Daelemans and Van den Bosch describe variations of this algorithm for use in [[natural language processing]] (NLP), claiming that memory-based learning is both more psychologically realistic than other machine-learning schemes and more effective in practice.<ref>Walter Daelemans and Antal van den Bosch (2005). ''Memory-Based Language Processing''. Cambridge University Press.</ref>

Gagliardi<ref name=Gagliardi2011>{{cite journal|last=Gagliardi|first=F|title=Instance-based classifiers applied to medical databases: Diagnosis and knowledge extraction|journal=Artificial Intelligence in Medicine|year=2011|volume=52|issue=3|pages=123-139|doi=10.1016/j.artmed.2011.04.002|url=http://dx.doi.org/10.1016/j.artmed.2011.04.002}}</ref> applies this family of classifiers in medical field as second-opinion [[Clinical decision support system|diagnostic tools]] and as tools for the knowledge extraction phase inthein the process of [[knowledge discovery in databases]].

One of these classifiers (called ''Prototype exemplar learning classifier'' ([[PEL-C]]) is able to extracts a mixture of abstracted prototypical cases (that are [[syndrome|syndromes]]) and selected atypical clinical cases.