Incremental learning with sample queries

Joel Ratsaby

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


The classical theory of pattern recognition assumes labeled examples appear according to unknown underlying class conditional probability distributions where the pattern classes are picked randomly in a passive manner according to their a priori probabilities. This paper presents experimental results for an incremental nearest-neighbor learning algorithm which actively selects samples from different pattern classes according to a querying rule as opposed to the a priori probabilities. The amount of improvement of this query-based approach over the passive batch approach depends on the complexity of the Bayes rule.

Original languageEnglish
Pages (from-to)883-888
Number of pages6
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number8
StatePublished - 1 Dec 1998
Externally publishedYes


  • Active learning
  • Incremental learning
  • Model selection
  • Nearestneighbor algorithm
  • Sample querying

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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