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

Associative Learning01:27

Associative Learning

1.2K
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...
1.2K
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.3K
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...
1.3K
Observational Learning01:12

Observational Learning

802
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...
802
Introduction to Learning01:18

Introduction to Learning

908
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...
908
Cognitive Learning01:21

Cognitive Learning

981
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...
981
Real-World Application of Classical Conditioning01:15

Real-World Application of Classical Conditioning

1.2K
Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
Higher-order, or second-order, conditioning occurs when a neutral stimulus becomes associated with an already established conditioned stimulus through repeated pairings. For instance, if a dog has been...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Changes in visceral adiposity, body composition, and cardiometabolic profiles following a supervised mixed exercise program in women with normal-weight obesity and obesity.

BMC sports science, medicine & rehabilitation·2026
Same author

PIP<sub>2</sub> activation of the cardiac I<sub>Ks</sub> potassium channel.

Nature communications·2026
Same author

Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment.

Biostatistics (Oxford, England)·2026
Same author

Soluble TREM1 Contributes to Aging-Related Neurodegeneration via ROBO2/ERK Pathway.

Molecular neurobiology·2026
Same author

Engineering novel AAV capsids by broadly attenuated and subsequent muscle-specific tropism in mice and NHPs.

Molecular therapy. Advances·2026
Same author

Efficient collaborative learning of the average treatment effect.

Biometrics·2026
Same journal

Accelerating Resident Research within Quantitative Collaboration Units in Academic Healthcare.

Stat (International Statistical Institute)·2025
Same journal

Multivariate differential association analysis.

Stat (International Statistical Institute)·2024
Same journal

Developing partnerships for academic data science consulting and collaboration units.

Stat (International Statistical Institute)·2024
Same journal

Deep learning models to predict primary open-angle glaucoma.

Stat (International Statistical Institute)·2024
Same journal

What is it that you say you do here? Advocating for the critical role of data scientists in research infrastructure.

Stat (International Statistical Institute)·2024
Same journal

Methods for building a staff workforce of quantitative scientists in academic health care.

Stat (International Statistical Institute)·2024
See all related articles
  1. Home
  3. On The Connections Among Three Transfer Learning Paradigms.
  1. Home
  3. On The Connections Among Three Transfer Learning Paradigms.

Related Experiment Video

Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another
05:12

Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another

Published on: September 18, 2017

548.5K

On the Connections Among Three Transfer Learning Paradigms.

Tian Gu1, Sijia Li2, Rui Duan3

  • 1Department of Biostatistics, Columbia Mailman School of Public Health, New York, New York, USA.

Stat (International Statistical Institute)
|November 26, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

We identified a general solution path for transfer learning estimators in linear models, revealing how they combine source and target data. This framework unifies existing methods and aids in developing new estimators for improved machine learning.

Keywords:
catalytic priorgradient descentimplicit regularizationpenalized regressiontransfer learning

More Related Videos

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.4K

Related Experiment Videos

Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another
05:12

Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another

Published on: September 18, 2017

548.5K
RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.4K

Area of Science:

  • Machine Learning
  • Statistical Modeling
  • Biostatistics

Background:

  • Transfer learning (TL) is crucial for leveraging existing data to improve model performance on new tasks.
  • Existing TL methods often lack a unified theoretical framework, hindering the design of novel estimators.
  • Understanding the interpolation between source and target estimators is key to advancing TL.

Purpose of the Study:

  • To analyze the solution paths of three transfer learning estimators in linear models.
  • To develop a general framework characterizing the interpolation between source and target estimators.
  • To demonstrate the practical utility of the proposed framework in a real-world application.

Main Methods:

  • Mathematical analysis of transfer learning estimator solution paths.
  • Identification of a general solution path involving basis changes and entry-wise weighting.
  • Extensive simulations to validate theoretical findings.
  • Application to end-stage renal disease risk prediction in Hispanic populations.

    • Main Results:

    • A general solution path for TL estimators in linear models was identified.
    • Existing TL methods were shown to be special cases of this general path.
    • The framework reveals connections and equivalences among different TL approaches.
    • Improved risk prediction for end-stage renal disease was achieved.

    Conclusions:

    • The proposed framework provides a unified understanding of TL estimators.
    • It offers insights for designing new TL methods with enhanced control.
    • The approach has potential for generalization to nonlinear models.
    • The practical application demonstrated significant improvements in a health disparity context.