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

Multiple Regression01:25

Multiple Regression

4.2K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
4.2K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

454
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 of...
454
Introduction to Learning01:18

Introduction to Learning

1.3K
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...
1.3K
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.6K
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
1.6K
Associative Learning01:27

Associative Learning

1.6K
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...
1.6K
Regression Toward the Mean01:52

Regression Toward the Mean

7.2K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
7.2K

You might also read

Related Articles

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

Sort by
Same author

Chromosomal scale genome assembly of medicinal plant Sophora tonkinensis.

BMC genomics·2026
Same author

Development of Chloroplast Microsatellite Markers and Assessment of Genetic Diversity and Population Structure of <i>Sophora tonkinensis</i> Gagnep. in Southwestern China.

Current issues in molecular biology·2026
Same author

Zhuyeqing Liquor Extract Ameliorates Oxidative Stress and Neuroinflammation in D-Galactose-Induced Aging Mice Model.

Foods (Basel, Switzerland)·2026
Same author

Effect of Mechanical Polishing on Rice Flavor: Comparison and Exploration of Key Aroma Characteristics Components.

Foods (Basel, Switzerland)·2026
Same author

Integrated Methylome and Transcriptome Analyses Reveal Methylation-Associated Cadmium Stress Responses in <i>Sophora tonkinensis</i>.

Plants (Basel, Switzerland)·2026
Same author

Mediating effect of psychological capital on the relationship between work engagement and perceived professional benefits among nursing interns: a cross-sectional study.

Frontiers in medicine·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
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

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

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

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

A Survey on Human-Centric Voice-Face Multimodal Learning.

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

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

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

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

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

Related Experiment Video

Updated: Mar 2, 2026

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

3.3K

A Self-Paced Regularization Framework for Multilabel Learning.

Changsheng Li, Fan Wei, Junchi Yan

    IEEE Transactions on Neural Networks and Learning Systems
    |May 24, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces multilabel self-paced learning (SPL), a new framework that prioritizes both labels and instances during training. This approach effectively enhances multilabel learning performance compared to existing methods.

    Related Experiment Videos

    Last Updated: Mar 2, 2026

    Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
    05:56

    Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

    Published on: April 14, 2023

    3.3K

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Data Science

    Background:

    • Multilabel learning assigns multiple labels to data instances.
    • Existing methods may not optimally handle the complexity of both labels and instances.
    • Self-paced learning (SPL) is a strategy that mimics human learning by starting with easier examples.

    Purpose of the Study:

    • To propose a novel multilabel learning framework incorporating the self-paced learning (SPL) scheme.
    • To introduce a new formulation for multilabel learning using an SPL regularizer.
    • To provide a general method for determining appropriate SPL functions for diverse multilabel scenarios.

    Main Methods:

    • Developed a multilabel self-paced learning (MSPL) framework.
    • Introduced a novel multilabel learning formulation with an SPL regularizer.
    • Designed a general approach to find suitable SPL functions for different learning tasks.

    Main Results:

    • The proposed MSPL framework effectively prioritizes label learning tasks and instances.
    • Experimental results on four datasets demonstrate the approach's effectiveness.
    • Outperformed existing state-of-the-art methods in multilabel learning tasks.

    Conclusions:

    • This work is the first to jointly consider instance and label complexities in multilabel learning via SPL.
    • The proposed MSPL framework offers a significant advancement in multilabel learning.
    • The method shows strong performance and effectiveness on public benchmark datasets.