A modification of the LAESA algorithm for approximated k-NN classification
Pattern Recognition Letters
Data mining tasks and methods: Classification: nearest-neighbor approaches
Handbook of data mining and knowledge discovery
New Algorithms for Efficient High-Dimensional Nonparametric Classification
The Journal of Machine Learning Research
A fast fuzzy $K$-nearest neighbour algorithm for pattern classification
Intelligent Data Analysis
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In this paper, we propose a method of designing a reduced complexity nearest-neighbor (RCNN) 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.