Nested hyper-rectangles for exemplar-based learning

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


Exemplar-based learning is a theory in which learning is accomplished by storing points in Euclidean n-space, En. This paper presents a new theory in which these points are generalized to become hyper-rectangles. These hyper-rectangles, in turn, may be nested to arbitrary depth inside one another. This representation scheme is sharply different from the usual inductive learning paradigms, which learn by replacing boolean formulae by more general formulae, or by creating decision trees. The theory is described and then compared to other inductive learning theories. An implementation, Each, has been tested empirically on three different domains: predicting the recurrence of breast cancer, classifying iris flowers, and predicting survival times for heart attack patients. In each case, the results are compared to published results using the same data sets and different machine learning algorithms. Each performs as well as or better than other algorithms on all of the data sets.

Original languageEnglish (US)
Title of host publicationAnalogical and Inductive Inference - International Workshop, All 1989, Proceedings
PublisherSpringer Verlag
Number of pages18
Volume397 LNAI
ISBN (Print)9783540517344
StatePublished - Jan 1 1989
Event2nd International Workshop on Analogical and Inductive Inference, AII 1989 - Reinhardsbrunn Castle, Germany
Duration: Oct 1 1989Oct 6 1989

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume397 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other2nd International Workshop on Analogical and Inductive Inference, AII 1989
CityReinhardsbrunn Castle

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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