The accuracy of the k-nearest neighbor algorithm depends on the distance function used to measure similarity between instances. Methods have been proposed in the literature to learn a good distance function from a labelled training set. One such method is the large margin nearest neighbor classifier that learns a global Mahalanobis distance. We propose a mixture of such classifiers where a gati...

Image segmentation is the classification of data sets into group of similar data points. This article proposed a method to determine the winner unit by self organizing mapping network. The distance between the input vector and the weight vector has been determined by mahalanobis distance and chooses the unit whose weight vector has the smallest mahalanobis distance from the input vector. The re...

in this paper, we aim to overcome three major shortcomings of the fdh (free disposal hull) directional distance function through developing two new, named linear and fractional cdfdh, complete fdh measures of efficiency. to accomplish this, we integrate the concepts of similarity and fdh directional distance function. we prove that the proposed measures are translation invariant and unit invari...

Distance-based classification methods such as the nearest-neighbor and k-nearest-neighbor classifiers have to rely on a metric or distance measure between points in the input space. For many applications, Euclidean distance in the input space is not a good choice and hence more complicated distance measures have to be used. In this paper, we propose a novel kernel-based method that achieves Euc...

In this article, a special type of orthogonalization is obtained to construct a multiple input transfer function model. By using this technique, construction of a transfer function model is divided to sequential construction of transfer function models with less input time series. Furthermore, based on real and simulated time series we provide an instruction to adequately perform the stages of ...

the index is excellent by the malmquist index as extended to productivity measurement. the index developed here is defined in terms of input cost rather than input quantity distance functions in supply chain. therefore, we propose productivity change is decomposed into overall efficiency and cost technical change. these decompositions provide a clearer situation of the root sources of supply ch...