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

1.9K
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...
1.9K
Introduction to Learning01:18

Introduction to Learning

1.6K
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...
1.6K
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.9K
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...
1.9K

You might also read

Related Articles

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

Sort by
Same author

MUFOLD-DB: a processed protein structure database for protein structure prediction and analysis.

BMC genomics·2015
Same author

The I-TASSER Suite: protein structure and function prediction.

Nature methods·2014
Same author

Genome-wide expression analysis of soybean NF-Y genes reveals potential function in development and drought response.

Molecular genetics and genomics : MGG·2014
Same author

Classification of lung cancer using ensemble-based feature selection and machine learning methods.

Molecular bioSystems·2014
Same author

Resveratrol possesses protective effects in a pristane-induced lupus mouse model.

PloS one·2014
Same author

Protein-losing enteropathy in systemic lupus erythematosus: 12 years experience from a Chinese academic center.

PloS one·2014

Related Experiment Video

Updated: Apr 4, 2026

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

1.3K

Learning With Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation.

Wen Li, Lixin Duan, Dong Xu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 10, 2015
    PubMed
    Summary

    This study introduces Heterogeneous Feature Augmentation (HFA) and semi-supervised HFA (SHFA) for heterogeneous domain adaptation (HDA). These methods effectively bridge domain gaps by transforming and augmenting features, outperforming existing HDA techniques.

    Related Experiment Videos

    Last Updated: Apr 4, 2026

    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

    1.3K

    Area of Science:

    • Machine Learning
    • Computer Vision
    • Data Science

    Background:

    • Heterogeneous domain adaptation (HDA) addresses challenges where source and target data have different feature dimensions.
    • Existing HDA methods often struggle with significant feature space discrepancies.
    • Bridging these feature gaps is crucial for effective cross-domain knowledge transfer.

    Purpose of the Study:

    • To propose novel methods for HDA that handle heterogeneous features.
    • To develop both supervised and semi-supervised approaches for improved domain adaptation.
    • To demonstrate the efficacy of the proposed methods on diverse applications.

    Main Methods:

    • Data transformation into a common subspace using projection matrices.
    • Augmenting features with original data and zeros to create unified representations.
    • Developing Heterogeneous Feature Augmentation (HFA) based on SVM.
    • Formulating HFA as a convex Multiple Kernel Learning (MKL) problem.
    • Introducing semi-supervised HFA (SHFA) to leverage unlabeled target data.

    Main Results:

    • HFA and SHFA successfully adapt models across domains with heterogeneous features.
    • The proposed methods demonstrate superior performance compared to existing HDA techniques.
    • SHFA effectively utilizes unlabeled target data for enhanced adaptation.
    • Experiments confirm the robustness and effectiveness across three distinct applications.

    Conclusions:

    • HFA and SHFA offer effective solutions for the HDA problem with heterogeneous features.
    • The MKL formulation guarantees a globally optimal solution for HFA.
    • SHFA provides a powerful framework for semi-supervised HDA.
    • The proposed methods represent a significant advancement in cross-domain adaptation research.