Statistical pattern recognition is a very active area of study andresearch, which has seen
many advances in recent years. New andemerging applications-such as data mining, web …

A conventional way to discriminate between objects represented by dissimilarities is the
nearest neighbor method. A more efficient and sometimes a more accurate solution is …

Nearest-neighbour (NN) and k-nearest-neighbours (k-NN) techniques are widely used in
many pattern recognition classification tasks. The linear approximating and eliminating …

A generalized prototype-based classification scheme founded on hierarchical clustering is
proposed. The basic idea is to obtain a condensed 1-NN classification rule by merging the …

In this paper, we present a fast and versatile algorithm which can rapidly perform a variety of
nearest neighbor searches. Efficiency improvement is achieved by utilizing the distance …

Nearest Neighbor rule is one of the most commonly used supervised classification
procedures due to its inherent simplicity and intuitive appeal. However, it suffers from the …

The nearest neighbor (NN) rule is usually chosen in a large number of pattern recognition
systems due to its simplicity and good properties. In particular, this rule has been …

The k nearest neighbor (k-NN) classifier has been a widely used nonparametric technique in
Pattern Recognition, because of its simplicity and good performance. In order to decide the …

In this paper, the efficiency of branch and bound search algorithms for the computation of K
nearest neighbors is studied. The most important aspects that influence the efficiency of the …

The learning process consists of different steps: building a Training Set (TS), training the
system, testing its behaviour and finally classifying unknown objects. When using a distance …