Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

Ogive Graph01:07

Ogive Graph

6.9K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
6.9K
Graphing Antiderivatives01:30

Graphing Antiderivatives

77
The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
77
Graphs of Functions01:30

Graphs of Functions

360
Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
360
Bar Graph01:07

Bar Graph

23.1K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
23.1K
Time-Series Graph00:54

Time-Series Graph

5.2K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.2K
Multiple Bar Graph01:07

Multiple Bar Graph

10.2K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
10.2K

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

Author Correction: Sub-technology market share strongly affects critical material constraints in power system transitions.

Nature communications·2026
Same author

Skin capillary endothelial cells form a network of spatiotemporally conserved Ca<sup>2+</sup> activity.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Unsupervised feature selection via row-sparse local preserving projection.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

A Unified Framework for Pseudo-Supervised Clustering via Weighted Sample Aggregation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Projection with mixed-size anchor graphs.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

SimMTC: Simple Multi-View Tensor Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

GoP-based Quality Enhancement on Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Ver todos los artículos relacionados

Video Experimental Relacionado

Updated: Feb 13, 2026

Collection and Long-Term Maintenance of Leaf-Cutting Ants Atta in Laboratory Conditions
10:11

Collection and Long-Term Maintenance of Leaf-Cutting Ants Atta in Laboratory Conditions

Published on: August 30, 2022

4.3K

Una estrategia codiciosa para el corte de gráficos.

Shenfei Pei, Huijuan Dong, Nianci Guan

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |February 11, 2026
    PubMed
    Resumen
    Este resumen es generado por máquina.

    Introducimos un algoritmo Greedy Graph Cut (GGC) para el particionamiento eficiente de gráficos. Este método determinista supera constantemente a los enfoques existentes en el problema del corte normalizado.

    Más Videos Relacionados

    Performing Microscope-Mounted Y-Shaped Cutting Tests
    06:15

    Performing Microscope-Mounted Y-Shaped Cutting Tests

    Published on: January 20, 2023

    4.8K
    New Variations for Strategy Set-shifting in the Rat
    09:45

    New Variations for Strategy Set-shifting in the Rat

    Published on: January 23, 2017

    8.6K

    Videos de Experimentos Relacionados

    Last Updated: Feb 13, 2026

    Collection and Long-Term Maintenance of Leaf-Cutting Ants Atta in Laboratory Conditions
    10:11

    Collection and Long-Term Maintenance of Leaf-Cutting Ants Atta in Laboratory Conditions

    Published on: August 30, 2022

    4.3K
    Performing Microscope-Mounted Y-Shaped Cutting Tests
    06:15

    Performing Microscope-Mounted Y-Shaped Cutting Tests

    Published on: January 20, 2023

    4.8K
    New Variations for Strategy Set-shifting in the Rat
    09:45

    New Variations for Strategy Set-shifting in the Rat

    Published on: January 23, 2017

    8.6K

    Área de la Ciencia:

    • Ciencias de la computación Ciencias de la computación
    • Ciencia de datos Ciencia de datos.
    • Aprendizaje automático Aprendizaje automático.

    Sus antecedentes:

    • La partición de gráficos es un problema fundamental en la informática con aplicaciones en varios campos.
    • Los algoritmos existentes a menudo sufren de sensibilidad a la inicialización aleatoria, lo que lleva a resultados inconsistentes.
    • Se necesitan métodos de partición de gráficos eficientes y deterministas para el análisis de datos a gran escala.

    Objetivo del estudio:

    • Proponer un nuevo algoritmo Greedy Graph Cut (GGC) para la partición de gráficos.
    • Para garantizar la partición de gráficos determinista y computacionalmente eficiente.
    • Para demostrar la efectividad de GGC en el problema del corte normalizado (N-Cut).

    Principales métodos:

    • El algoritmo Greedy Graph Cut (GGC) fusiona iterativamente los clusters para minimizar una función objetivo global.
    • Las operaciones de fusión están restringidas a grupos adyacentes para mejorar la eficiencia computacional.
    • Se proporciona una prueba teórica de la convergencia monotónica de la función objetivo.

    Principales resultados:

    • GGC demuestra convergencia determinista, lo que garantiza resultados consistentes en múltiples ejecuciones.
    • El algoritmo exhibe una escala casi lineal de la complejidad computacional con el tamaño de la muestra.
    • GGC consistentemente supera el rendimiento de la propia composición convencional seguida de un enfoque de agrupación de medios k para N-Cut.
    • Los análisis comparativos muestran que GGC supera a varios algoritmos de agrupación de clusters de última generación.

    Conclusiones:

    • El algoritmo Greedy Graph Cut (GGC) propuesto ofrece una solución efectiva y eficiente para la partición de gráficos.
    • GGC proporciona una alternativa determinista a los métodos existentes, asegurando resultados confiables.
    • GGC muestra un rendimiento superior en la resolución del problema de corte normalizado (N-Cut) en comparación con las técnicas establecidas.