Fast design of reduced-complexity nearest-neighbor classifiersusing triangular inequality
Eel-Wan Lee
Soo-Ik Chae
Sch. of Electr. Eng., Seoul Nat. Univ.;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: May 1998
Volume: 20,
Issue: 5
On page(s): 562-566
ISSN: 0162-8828
References Cited: 11
CODEN: ITPIDJ
INSPEC Accession Number: 5949134
Digital Object Identifier: 10.1109/34.682187
Current Version Published: 2002-08-06
Abstract
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
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