Two Modifications of CNN

@inproceedings{Tomek1976TwoMO,
  title={Two Modifications of CNN},
  author={Ivan Tomek},
  year={1976}
}

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References

SHOWING 1-9 OF 9 REFERENCES
Introcuction to FORTRAN IV
  • Introcuction to FORTRAN IV
  • 1976
An algorithm for a selective nearest neighbor decision rule (Corresp.)
A procedure is introduced to approximate nearest neighbor (INN) decision boundaries. The algorithm produces a selective subset of the original data so that 1) the subset is consistent, 2) theExpand
Pattern classification and scene analysis
  • R. Duda, P. Hart
  • Computer Science, Mathematics
  • A Wiley-Interscience publication
  • 1973
TLDR
The topics treated include Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis. Expand
Asymptotic Properties of Nearest Neighbor Rules Using Edited Data
The convergence properties of a nearest neighbor rule that uses an editing procedure to reduce the number of preclassified samples and to improve the performance of the rule are developed. Editing ofExpand
The reduced nearest neighbor decision rule
  • IEEE Trans. Inform. Theoryj
  • 1972
The reduced nearest neighbor rule (Corresp.)
  • G. Gates
  • Computer Science
  • IEEE Trans. Inf. Theory
  • 1972
A further modification to Cover and Hart's nearest neighbor decision rule, the reduced nearest neighbor rule, is introduced. Experimental results demonstrate its accuracy and efficiency.
The condensed nearest neighbor
  • IEEE Trans. Inform. Theory
  • 1968
The condensed nearest neighbor rule (Corresp.)
  • P. Hart
  • Computer Science
  • IEEE Trans. Inf. Theory
  • 1968