Jove
Visualize
Contáctanos

Videos de Conceptos Relacionados

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

149
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
149
Associative Learning01:27

Associative Learning

572
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
572
Observational Learning01:12

Observational Learning

311
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
311
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

252
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
252
Modeling in Therapy01:26

Modeling in Therapy

145
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
145
Typical Model Studies01:30

Typical Model Studies

440
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
440

También podría leer

Artículos Relacionados

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

Ordenar por
Same author

Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning.

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

Dual-Branch Aesthetic Image Retouching via Active Reinforcement Learning for Color Enhancement and Composition Optimization.

IEEE transactions on visualization and computer graphics·2026
Same author

Prototypes as Anchors: Tackling Unseen Noise for online continual learning.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Image Lens Flare Removal Using Adversarial Curve Learning.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Aesthetics-Guided Low-Light Enhancement.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Continual learning in the presence of repetition.

Neural networks : the official journal of the International Neural Network Society·2024
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

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

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

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

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

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

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

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

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

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

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
Ver todos los artículos relacionados
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: Sep 10, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

681

Aprendizaje activo para modelos de objetivos múltiples

Sheng-Jun Huang, Yi Li, Ying-Peng Tang

    IEEE transactions on pattern analysis and machine intelligence
    |August 25, 2025
    PubMed
    Resumen
    Este resumen es generado por máquina.

    Este estudio introduce un nuevo enfoque de aprendizaje activo (AL) para entrenar múltiples modelos de aprendizaje automático simultáneamente. La nueva estrategia de AL agnóstica mejora la eficiencia de la consulta al seleccionar puntos de datos en los que los modelos no están de acuerdo.

    Más Videos Relacionados

    An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles
    09:27

    An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles

    Published on: August 25, 2020

    4.3K
    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
    06:37

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

    Published on: December 15, 2023

    4.1K

    Videos de Experimentos Relacionados

    Last Updated: Sep 10, 2025

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    681
    An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles
    09:27

    An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles

    Published on: August 25, 2020

    4.3K
    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
    06:37

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

    Published on: December 15, 2023

    4.1K

    Área de la Ciencia:

    • Aprendizaje automático
    • Inteligencia artificial
    • Ciencia de los datos

    Sus antecedentes:

    • Los métodos tradicionales de aprendizaje activo (AL) a menudo dependen del modelo y carecen de transferibilidad.
    • Las aplicaciones del mundo real con frecuencia requieren entrenar múltiples modelos para diversos recursos computacionales.
    • Los enfoques de AL existentes se enfrentan a desafíos en los escenarios de aprendizaje de múltiples modelos.

    Objetivo del estudio:

    • Investigar la viabilidad de diseñar métodos eficaces de aprendizaje activo para el aprendizaje simultáneo de múltiples modelos objetivo.
    • Analizar la complejidad de la consulta del aprendizaje activo frente al pasivo en un entorno de varios modelos.
    • Desarrollar una nueva estrategia de AL aplicable a diversos modelos de aprendizaje automático.

    Principales métodos:

    • Análisis de la complejidad de la consulta para el aprendizaje activo y pasivo en el entorno de múltiples modelos.
    • Propuesta de una estrategia de muestreo de aprendizaje activo.
    • Selección de puntos de datos de regiones de desacuerdo común entre diferentes modelos objetivo.

    Principales resultados:

    • Potencial demostrado para el aprendizaje activo para lograr una mayor complejidad de la consulta en el aprendizaje multimodelo.
    • Validación de la eficacia de la estrategia de muestreo de AL agnóstico propuesta.
    • Los resultados experimentales muestran un rendimiento superior en comparación con los métodos AL tradicionales en conjuntos de datos de referencia.

    Conclusiones:

    • Se puede diseñar un método de aprendizaje activo eficaz para el aprendizaje simultáneo de múltiples modelos objetivo.
    • La estrategia de AL agnóstica propuesta ofrece una dirección prometedora para una formación multimodelo eficiente.
    • Este enfoque mejora la eficiencia de los datos en los sistemas de aprendizaje automático con diversos requisitos de modelo.