The Production Possibility Set (PPS) is defined as the set of all inputs and outputs of a system in which inputs can produce outputs‎. ‎In Data Envelopment Analysis (DEA)‎, ‎it is highly important to identify the defining hyperplanes and especially the strong defining hyperplanes of the empirical PPS‎. ‎Although DEA models can determine the efficiency of a Decision Making Unit (DMU)‎, ‎but they...

this paper investigates a procedure for identifying all efficient hyperplanes of production possibility set (pps). this procedure is based on a method which recommended by pekka j. korhonen[8]. he offered using of lexicographic parametric programming method for recognizing all efficient units in data envelopment analysis (dea). in this paper we can find efficient hyperplanes, via using the para...

This paper investigates a procedure for identifying all efficient hyperplanes of production possibility set (PPS). This procedure is based on a method which recommended by Pekka J. Korhonen[8]. He offered using of lexicographic parametric programming method for recognizing all efficient units in data envelopment analysis (DEA). In this paper we can find efficient hyperplanes, via using the para...

in this paper, the cost and income efficiency models have been considered with regard to the multiple objective programming structures. for finding the efficient points in purposed molp problem, some various methods like lexicography & the weighted sum can be used. so by introducing the molp problem the cost & income efficiencies will be achieved. in the present study, the molp problem ...

In this paper, the cost and income efficiency models have been considered with regard to the multiple objective programming structures. For finding the efficient points in purposed MOLP problem, some various methods like Lexicography & the weighted sum can be used. So by introducing the MOLP problem the cost & income efficiencies will be achieved. In the present study, the MOLP problem is conve...

Motivated by nonparallel hyperplanes support vector machine (NHSVM), a new regression method of data, named as nonparallel hyperplanes support vector regression (NHSVR), is proposed in this paper. The advantages of NHSVR have two aspects, one is considering the minimization of structure risk by introducing a regularization term in objective function, and another is finding two nonparallel hyper...

We present a classifier algorithm that approximates the decision surface of labeled data by a patchwork of separating hyperplanes. The hyperplanes are arranged in a way inspired by how Self-Organizing Maps are trained. We take advantage of the fact that the boundaries can often be approximated by linear ones connected by a low-dimensional nonlinear manifold. The resulting classifier allows for ...

Finding nearest neighbor points in a large scale high dimensional data set is of wide interest in computer vision. One popular and efficient approach is to encode each data point as a binary code in Hamming space using separating hyperplanes. One condition which is often implicitly assumed is that the separating hyperplanes should be mutually orthogonal. With the aim of increasing the represent...

The ability of determining all defining hyperplanes of DEA production possibility set (efficient frontier) prior to the DEA computations is of extreme importance. Specially, access to efficient frontier permits a complete analysis (e.g. calculation of efficiency scores, returns to scale, sensitivity analysis and so on) in second phase for the corresponding model. This paper presents a linear sy...