  In this paper, a novel K-Nearest Neighbour and Support Vector Machine hybrid
classification technique has been proposed that is simple and robust. It is
based on the concept of discriminative nearest neighbourhood classification.
The technique consists of using K-Nearest Neighbour Classification for test
samples satisfying a proximity condition. The patterns which do not pass the
proximity condition are separated. This is followed by sifting the training set
for a fixed number of patterns for every class which are closest to each
separated test pattern respectively, based on the Euclidean distance metric.
Subsequently, for every separated test sample, a Support Vector Machine is
trained on the sifted training set patterns associated with it, and
classification for the test sample is done. The proposed technique has been
compared to the state of art in this research area. Three datasets viz. the
United States Postal Service (USPS) Handwritten Digit Dataset, MNIST Dataset,
and an Arabic numeral dataset, the Modified Arabic Digits Database, MADB, have
been used to evaluate the performance of the algorithm. The algorithm generally
outperforms the other algorithms with which it has been compared.
