Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

143
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...
143
Observational Learning01:12

Observational Learning

285
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...
285
Introduction to Learning01:18

Introduction to Learning

519
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
519
Associative Learning01:27

Associative Learning

538
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...
538
Cognitive Learning01:21

Cognitive Learning

486
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
486
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

233
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,...
233

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Entropy-Driven Nonmetal Doping for Electrocatalysis and Energy Storage.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Bridging Acidic and Alkaline Hydrogen Electrocatalysis via Ru-Modified Pt<sub>3</sub>Co for Waste-to-Energy Electrochemical Neutralization Fuel Cells.

Advanced materials (Deerfield Beach, Fla.)·2025
Same author

"Dynamic Ensemble, Then Knowledge Distillation": A SHAP-Driven Two-Stage Framework for Sepsis Mortality Prediction.

IEEE journal of biomedical and health informatics·2025
Same author

Engineering High-Density Grain Boundaries in Ru<sub>0.8</sub>Ir<sub>0.2</sub>O<sub>x</sub> Solid-Solution Nanosheets for Efficient and Durable OER Electrocatalysis.

Advanced materials (Deerfield Beach, Fla.)·2025
Same author

Electrochemical Pilot H<sub>2</sub>O<sub>2</sub> Production by Solid-State Electrolyte Reactor: Insights From a Hybrid Catalyst for 2-Electron Oxygen Reduction Reaction.

Angewandte Chemie (International ed. in English)·2025
Same author

Nonmetallic High-Entropy-Engineered Nanocarbons for Advanced ORR Electrocatalysis.

Angewandte Chemie (International ed. in English)·2025
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
Same journal

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

IEEE transactions on neural networks and learning systems·2026
Same journal

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

IEEE transactions on neural networks and learning systems·2026
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Aug 29, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

457

A Unified Framework Based on Graph Consensus Term for Multiview Learning.

Xiangzhu Meng, Lin Feng, Chonghui Guo

    IEEE Transactions on Neural Networks and Learning Systems
    |September 12, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a unified Graph Consensus Multiview Framework (GCMF) for machine learning. GCMF effectively integrates diverse graph-based methods to enhance scalability and robustness in multiview learning tasks.

    More Related Videos

    Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
    07:12

    Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

    Published on: April 11, 2025

    535
    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    20.0K

    Related Experiment Videos

    Last Updated: Aug 29, 2025

    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    457
    Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
    07:12

    Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

    Published on: April 11, 2025

    535
    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    20.0K

    Area of Science:

    • Machine Learning
    • Data Science
    • Artificial Intelligence

    Background:

    • Multiview learning methods often lack scalability and robustness for diverse applications.
    • Existing single-view methods are difficult to extend to multiview scenarios.
    • Graph-based algorithms are effective for learning complex data structures.

    Purpose of the Study:

    • To propose a unified and scalable multiview learning framework.
    • To leverage existing graph embedding works for enhanced multiview analysis.
    • To address the challenges of efficiently handling diverse and complex multiview data.

    Main Methods:

    • Introduced a graph consensus term to unify existing graph embedding methods.
    • Developed the Graph Consensus Multiview Framework (GCMF).
    • Implemented GCMF with an extension of Locality Linear Embedding (LLE), named GCMF-LLE, using alternating optimization.

    Main Results:

    • GCMF preserves the diversity of graph embedding methods by exploring individual view structures.
    • The graph consensus term stably explores correlations among multiple views.
    • Empirical validation on six benchmark datasets demonstrated the effectiveness of GCMF-LLE.

    Conclusions:

    • GCMF offers a scalable and robust solution for multiview learning.
    • The framework effectively considers both diversity and complementary information across views.
    • GCMF provides a flexible approach for integrating various graph-based techniques in multiview settings.