Referenced Items are not available for this document.
We propose a method of designing a reduced complexity nearest-neighbor classifier with near-minimal computational complexity from a given nearest-neighbor classifier that has high input dimensionality and a large number of class vectors. We applied our method to the classification problem of handwritten numerals in the NIST database. If the complexity of the RCNN classifier is normalized to that of the given classifier, the complexity of the derived classifier is 62 percent, 2 percent higher than that of the optimal classifier. This was found using the exhaustive search
Published in:
Pattern Analysis and Machine Intelligence, IEEE Transactions on
(Volume:20
,
Issue:
5
)
Date of Publication: May 1998
- Page(s):
- 562 - 566
- ISSN :
- 0162-8828
- INSPEC Accession Number:
- 5949134
- Digital Object Identifier :
- 10.1109/34.682187
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 06 August 2002
- Issue Date :
- May 1998
- Sponsored by :
- IEEE Computer Society
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