Bibtex entry:

@article{nc:Rov95,
   keywords = {ocr, pattern recognition, nearest neighbors,
               algorithms, neural networks},
   author = {R. Rovatti and R. Ragazzoni and Zs. M. Kovacs and R. Guerrieri},
   title="Adaptive Voting Rules fo k-NN Classifiers",
   key={nc:Rov95},
   journal={Neural Computation},
   press = {MIT},
   volume = {7},
   number = {3},
   pages = {594--605},
   month = {May},
   year = {1995},
   abstract={
A simple form of cooperation between the k-Nearest Neighbors approach
to classification and the neural-like property of adaptation is
explored. A tunable, high level k-Nearest Neighbors decision rule is
defined which comprehends most previous generalizations of the common
majority rule.  A learning procedure is developed which applies to
this rule and exploits those statistical features that can be induced
from the training set. The overall approach is tested on a problem of
handwritten character recognition.  Performance measurements show that
adaptivity in the decision rule may greatly improve the recognition
ability of standard k-NN classifiers.
},
}

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