Jove
Visualize
Contáctanos

Videos de Conceptos Relacionados

Counterfactual Thinking01:19

Counterfactual Thinking

255
Counterfactual thinking is a cognitive process wherein individuals mentally reconstruct alternative versions of past events, often beginning with “what if” or “if only.” This reflective mechanism plays a significant role in shaping emotional experiences and guiding future behavior. Though typically triggered by unfavorable or unexpected outcomes, counterfactual thinking can also emerge in mundane, everyday decisions and experiences, revealing its deep entrenchment in...
255
Drug Classes and Categories01:25

Drug Classes and Categories

3.1K
Drugs can be classified according to their chemical composition or their intended therapeutic application. For instance, anti-infective agents that possess the ability to eliminate pathogens or suppress their growth and reproduction can be grouped based on the organisms they target or their chemical structure. Furthermore, drugs can be divided into prescription, nonprescription, or controlled substances. Prescription medications, such as antibiotics, require oversight from a licensed healthcare...
3.1K
Antibody Structure and Classes01:25

Antibody Structure and Classes

9.2K
Antibodies, also known as immunoglobulins, are produced by B cells in response to foreign substances, such as bacteria and viruses. These proteins are critical for recognizing and neutralizing these substances, protecting the body from potential harm.
The basic structure of an antibody consists of four protein chains: two identical heavy chains and two identical light chains. These chains are held together by disulfide bonds and other non-covalent interactions, forming a Y-shaped structure.
9.2K
Force Classification01:22

Force Classification

2.4K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.4K
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

5.3K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
5.3K
Classification of Leukocytes01:30

Classification of Leukocytes

5.9K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
5.9K

También podría leer

Artículos Relacionados

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

Ordenar por
Same author

Vagus nerve stimulation alleviates anxiety by inhibiting ferroptosis-related neuronal damage through α7nAChR.

International immunopharmacology·2026
Same author

Aligning Chemical Kinetics with Crystallization Enables Millimeter-Scale Single Crystals of Conductive MOFs.

Journal of the American Chemical Society·2026
Same author

Programming stacking order in conducting van der Waals metal-organic frameworks through ligand aggregation.

Nature chemistry·2026
Same author

Causal link between folic acid and recurrent aphthous ulcers: A two-sample Mendelian randomization study.

Medicine·2026
Same author

Precise aggressive aerial maneuvers with sensorimotor policies.

Science robotics·2026
Same author

Sigma-1R-CD36 axis in myeloid cells contributes to the alleviation of depression-like behaviors.

Journal of translational medicine·2026
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

Video Experimental Relacionado

Updated: Feb 6, 2026

Visualizing Antigen Specific CD4+ T Cells using MHC Class II Tetramers
15:42

Visualizing Antigen Specific CD4+ T Cells using MHC Class II Tetramers

Published on: March 6, 2009

22.2K

Repulsión adaptativa de muestras contra contraejemplos específicos de clase para clasificación desequilibrada

Yu Hao1, Xin Gao1, Xinping Diao2

  • 1School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, Beijing, 100876, China.

Neural networks : the official journal of the International Neural Network Society
|February 4, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta un marco novedoso para la clasificación desequilibrada que mejora el rendimiento del modelo en espacios de características superpuestos. El método repele adaptativamente las muestras contra contraejemplos específicos de clase, mejorando la precisión de la clasificación y la credibilidad del modelo.

Palabras clave:
búsqueda contrafactualaprendizaje automático explicableclasificación desequilibradasuperposición interclasecontrol de distribución de muestras

Más Videos Relacionados

High-Throughput Measurement and Classification of Organic P in Environmental Samples
08:58

High-Throughput Measurement and Classification of Organic P in Environmental Samples

Published on: June 8, 2011

13.4K
Determination of Total Lipid and Lipid Classes in Marine Samples
14:59

Determination of Total Lipid and Lipid Classes in Marine Samples

Published on: December 11, 2021

5.3K

Videos de Experimentos Relacionados

Last Updated: Feb 6, 2026

Visualizing Antigen Specific CD4+ T Cells using MHC Class II Tetramers
15:42

Visualizing Antigen Specific CD4+ T Cells using MHC Class II Tetramers

Published on: March 6, 2009

22.2K
High-Throughput Measurement and Classification of Organic P in Environmental Samples
08:58

High-Throughput Measurement and Classification of Organic P in Environmental Samples

Published on: June 8, 2011

13.4K
Determination of Total Lipid and Lipid Classes in Marine Samples
14:59

Determination of Total Lipid and Lipid Classes in Marine Samples

Published on: December 11, 2021

5.3K

Área de la Ciencia:

  • Aprendizaje automático
  • Ciencia de datos
  • Inteligencia artificial

Sus antecedentes:

  • La clasificación desequilibrada presenta desafíos en espacios de características complejos con regiones de muestras superpuestas.
  • Los métodos existentes a menudo no logran modelar profundamente las relaciones entre características y etiquetas o proporcionar explicaciones a nivel de instancia.
  • Esto limita las mejoras en el rendimiento de la clasificación y la credibilidad del modelo.

Objetivo del estudio:

  • Proponer un marco de clasificación desequilibrada explicable (CSCF-SR) que regule dinámicamente la distribución del espacio de características.
  • Formar un circuito cerrado entre la generación de explicaciones y las decisiones de clasificación utilizando muestras contrafactuales.
  • Mejorar la capacidad de clasificación del modelo para muestras en regiones superpuestas.

Principales métodos:

  • Una arquitectura de aprendizaje por refuerzo de doble actor específica de clase para la búsqueda contrafactual.
  • Un mecanismo de perturbación dinámica de varios pasos para la generación precisa de muestras contrafactuales.
  • Repulsión adaptativa de muestras que explota vectores de desplazamiento para aclarar los límites de clase.

Principales resultados:

  • CSCF-SR demostró un rendimiento superior a 27 métodos de clasificación desequilibrada en la puntuación F1 y la G-mean en 50 conjuntos de datos.
  • Se observaron mejoras significativas en 25 conjuntos de datos con superposición severa de clases.
  • El marco mejora eficazmente la clasificación de muestras dentro de regiones superpuestas.

Conclusiones:

  • El marco propuesto CSCF-SR ofrece un enfoque novedoso para la clasificación desequilibrada al integrar la explicabilidad y la manipulación adaptativa de muestras.
  • El método muestra ganancias significativas de rendimiento, particularmente en escenarios desafiantes con alta superposición de clases.
  • Este trabajo contribuye a modelos de clasificación desequilibrada más creíbles y precisos.