Traducción y análisis de palabras por inteligencia artificial ChatGPT
En esta página puede obtener un análisis detallado de una palabra o frase, producido utilizando la mejor tecnología de inteligencia artificial hasta la fecha:
- cómo se usa la palabra
- frecuencia de uso
- se utiliza con más frecuencia en el habla oral o escrita
- opciones de traducción
- ejemplos de uso (varias frases con traducción)
- etimología
fuzzy set theory - traducción al Inglés
Ex: The object of applying fuzzy set theory to information retrieval systems is that, by using a computer, we may be able to devise a system that will allow us to rank documents retrieved in probable order of relevance.
Ex: Various other methods of obtaining clusters have been described, including the use of fuzzy sets, but these are beyond the scope of this book.
Ex: This is a rather fuzzy basis for establishing subject headings, but fuzziness is not the guidelines only fault.
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* fuzzy clustering = conjuntos de lógica difusa
* fuzzy database = base de datos de lógica difusa
* fuzzy data retrieval = recuperación de información de lógica difusa
* fuzzy IR technique = técnica de recuperación por medio de la lógica difusa
* fuzzy logic = lógica difusa, lógica imprecisa, lógica borrosa
* fuzzy match = búsqueda difusa
* fuzzy matching = búsqueda difusa
* fuzzy model = modelo de lógica difusa
* fuzzy query processor = procesador de lógica difusa
* fuzzy random variable = variable aleatoria de lógica difusa
* fuzzy set = conjunto de lógica difusa
* fuzzy set theory = teoría de conjuntos difusos
* fuzzy system = sistema de lógica difusa
* fuzzy term = término impreciso
* fuzzy-valued = con valores difusos
* random fuzzy set = conjunto aleatorio de lógica difusa
* thresholded fuzzy clustering = ?
Definición
Wikipedia
In mathematics, fuzzy sets (a.k.a. uncertain sets) are sets whose elements have degrees of membership. Fuzzy sets were introduced independently by Lotfi A. Zadeh in 1965 as an extension of the classical notion of set. At the same time, Salii (1965) defined a more general kind of structure called an L-relation, which he studied in an abstract algebraic context. Fuzzy relations, which are now used throughout fuzzy mathematics and have applications in areas such as linguistics (De Cock, Bodenhofer & Kerre 2000), decision-making (Kuzmin 1982), and clustering (Bezdek 1978), are special cases of L-relations when L is the unit interval [0, 1].
In classical set theory, the membership of elements in a set is assessed in binary terms according to a bivalent condition—an element either belongs or does not belong to the set. By contrast, fuzzy set theory permits the gradual assessment of the membership of elements in a set; this is described with the aid of a membership function valued in the real unit interval [0, 1]. Fuzzy sets generalize classical sets, since the indicator functions (aka characteristic functions) of classical sets are special cases of the membership functions of fuzzy sets, if the latter only takes values 0 or 1. In fuzzy set theory, classical bivalent sets are usually called crisp sets. The fuzzy set theory can be used in a wide range of domains in which information is incomplete or imprecise, such as bioinformatics.
