In recent years, learning on manifolds has attracted much attention in the academia community. The idea that the distribution of real-life data forms a low dimensional manifold embedded in the ambient space works quite well in practice, with applications such as ranking, dimensionality reduction, semi-supervised learning and clustering. This paper focuses on ranking on manifolds. Traditional ma...

In this paper we propose a new method for ranking Z- numbers and generalizations. This method is based on the internal structure of the artificial neural network, which suggests that the structure of this network consists of inputs weights and the transfer function linear, nonlinear and sometimes linear and nonlinear. It is shown that the proposed method while possessing the ranking properties ...

The choice of suitable robots in manufacturing, to improve product quality and to increase productivity, is a complicated decision due to the increase in robot manufacturers and configurations. In this article, a novel approach is proposed to choose among alternatives, differently assessed by decision makers on different criteria, to make the final evaluation for decision-making. The approach i...

In this thesis, we describe various experiments on the ranking function of the Google Search Appliance to improve search quality. An evolutionary computation framework is implemented and applied to optimize various parameter settings of the ranking function. We evaluate the importance of IDF in the ranking function and achieve small improvements in performance. We also examine many ways to comb...

envelopment analysis (dea) is a very eective method to evaluate the relative eciency of decision-making units (dmus). dea models divided all dmus in two categories: ecient and inecientdmus, and don't able to discriminant between ecient dmus. on the other hand, the observedvalues of the input and output data in real-life problems are sometimes imprecise or vague, suchas interval data, ...

In this thesis, we describe various experiments on the ranking function of the Google Search Appliance to improve search quality. An evolutionary computation framework is implemented and applied to optimize various parameter settings of the ranking function. We evaluate the importance of IDF in the ranking function and achieve small improvements in performance. We also examine many ways to comb...

The problem of ranking, in which the goal is to learn a real-valued ranking function that induces a ranking or ordering over an instance space, has recently gained attention in machine learning. We study generalization properties of ranking algorithms, in a particular setting of the ranking problem known as the bipartite ranking problem, using the notion of algorithmic stability. In particular,...

Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learn the ranking scores from data points plays an important role. Up to new, these two methods have always been used individually, assuming that data coding and ranking are two independent and irrelev...

Abstract This paper proposes and characterizes a method to solve multicriteria evaluation problems when individual judgements are categorical may fail satisfy both transitivity completeness. The function consists of weighted sum the average number times that each alternative precedes some other, in all pairwise comparisons. It provides, therefore, quantitative assessment which is well-grounded,...