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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
Qué (quién) es L2 Syntactic Complexity Analyzer - definición
L2 Syntactic Complexity Analyzer
L2 Syntactical Complexity Analyzer (L2SCA) developed by Xiaofei Lu at the Pennsylvania State University, is a computational tool which produces syntactic complexity indices of written English language texts. Along with Coh-Metrix, the L2SCA is one of the most extensively used computational tool to compute indices of second language writing development.
Computational complexity
MEASURE OF THE AMOUNT OF RESOURCES NEEDED TO RUN AN ALGORITHM OR SOLVE A COMPUTATIONAL PROBLEM
Asymptotic complexity; Computational Complexity; Bit complexity; Context of computational complexity; Complexity of computation (bit); Computational complexities
In computer science, the computational complexity or simply complexity of an algorithm is the amount of resources required to run it. Particular focus is given to computation time (generally measured by the number of needed elementary operations) and memory storage requirements.
complexity
PROFESSIONAL ESPORTS ORGANIZATION BASED IN THE UNITED STATES
Los Angeles Complexity; CompLexity Gaming; LA Complexity; Complexity LA; CompLexity; Team CompLexity; CoL.Black; CoL
<algorithm> The level in difficulty in solving mathematically
posed problems as measured by the time, number of steps or
arithmetic operations, or memory space required (called time
complexity, computational complexity, and space complexity,
respectively).
The interesting aspect is usually how complexity scales with
the size of the input (the "scalability"), where the size of
the input is described by some number N. Thus an algorithm
may have computational complexity O(N^2) (of the order of the
square of the size of the input), in which case if the input
doubles in size, the computation will take four times as many
steps. The ideal is a constant time algorithm (O(1)) or
failing that, O(N).
See also NP-complete.
(1994-10-20)
