Results 241 to 250 of about 4,831,666 (266)
Some of the next articles are maybe not open access.
The Loss Distance Function: Economic Inefficiency Decompositions
2022From the beginning of DEA as a well-defined multi-output-multi-input tool for measuring efficiency, a huge number of technical efficiency measures have been introduced in the literature. Each of them implements a different way of gauging the “distance” from a firm in the interior of the technology to its efficient frontier (radially, hyperbolically ...
Pastor, Jesús T. +2 more
openaire +1 more source
Distances of Fuzzy Choice Functions
New Mathematics and Natural Computation, 2015In this paper, we introduce four distances on the set of fuzzy choice functions defined on a finite choice space. They are studied along with four distances on the set of fuzzy relations. The two types of distance allow to investigate the way the changes in fuzzy preferences are reflected in the changes of fuzzy choice associated with them.
Irina Georgescu, Jani Kinnunen
openaire +2 more sources
1995
The thought that firms are restricted by cost or revenue targets is developed in this chapter. Cost and revenue indirect distance functions are introduced in Section 4.1. These functions are related to the cost and revenue functions in Chapter Three, respectively.
Rolf Färe, Daniel Primont
openaire +1 more source
The thought that firms are restricted by cost or revenue targets is developed in this chapter. Cost and revenue indirect distance functions are introduced in Section 4.1. These functions are related to the cost and revenue functions in Chapter Three, respectively.
Rolf Färe, Daniel Primont
openaire +1 more source
The Hölder Distance Functions: Economic Inefficiency Decompositions
2022Data Envelopment Analysis can determine both a technical efficiency score and benchmarking information on how to change inputs and outputs to reach the efficient frontier if the firm under evaluation is technically inefficient. All measures studied in this book resort to the determination of benchmarking information through the calculation of the ...
Pastor, Jesús T. +2 more
openaire +1 more source
2003
In the last chapter we defined implicit functions with φ(x↦) ≤ 0 in the interior region Ω-, φ((x↦) > 0 in the exterior region Ω+, and φ((x↦) = 0 on the boundary ∂Ω. Little was said about φ otherwise, except that smoothness is a desirable property especially in sampling the function or using numerical approximations.
Stanley Osher, Ronald Fedkiw
openaire +1 more source
In the last chapter we defined implicit functions with φ(x↦) ≤ 0 in the interior region Ω-, φ((x↦) > 0 in the exterior region Ω+, and φ((x↦) = 0 on the boundary ∂Ω. Little was said about φ otherwise, except that smoothness is a desirable property especially in sampling the function or using numerical approximations.
Stanley Osher, Ronald Fedkiw
openaire +1 more source
Height Functions and Distance Functions
1997We now restrict our attention to two classes of functions on surfaces, which have particular importance in computer geometry: height functions and distance functions. In particular, we investigate when such functions are Morse functions. We consider a solid, bounded, connected object in ℝ3, as in Figure 9.1, and assume that it has a smooth boundary ...
Anatolij T. Fomenko, Tosiyasu L. Kinii
openaire +1 more source
Directional vs. Shephard’s distance functions
Optimization Letters, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +3 more sources
FUNCTIONAL DISTANCE MEDIATES PLANT INTERACTIONS
Journal of Vegetation ScienceThe outcome of plant interactions is strongly dependent on the set of physiological, morphological, and reproductive traits harboured by the interacting species. Two trait-based mechanisms are behind these plant interactions: a) trait dissimilarities, when species with similar traits compete more due to niche overlap, and b) trait hierarchies, when ...
Julia Jimeno‐Alda +3 more
openaire +1 more source
Formulating Distance Functions
2011Tasks of data mining and information retrieval depend on a good distance function for measuring similarity between data instances. The most effective distance function must be formulated in a context-dependent (also application-, data- and user-dependent) way.
openaire +1 more source
Context-Sensitive Kernel Functions: A Distance Function Viewpoint
2006This paper extends the idea of weighted distance functions to kernels and support vector machines. Here, we focus on applications that rely on sliding a window over a sequence of string data. For this type of problems it is argued that a symbolic, context-based representation of the data should be preferred over a continuous, real format as this is a ...
Manderick, Bernard +2 more
openaire +2 more sources