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

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
Probability Distributions01:32

Probability Distributions

11.8K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
11.8K
Classification of Systems-I01:26

Classification of Systems-I

552
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
552
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.8K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.8K
Classification of Systems-II01:31

Classification of Systems-II

458
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
458
Aggregates Classification01:29

Aggregates Classification

970
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
970

You might also read

Related Articles

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

Sort by
Same author

Multisource Machine Learning Model for Detecting Referral-Warranted Retinopathy of Prematurity.

Ophthalmology science·2026
Same author

Liposomes containing histidine overcome poly ADP-ribose polymerase inhibitor resistance.

Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy·2026
Same author

The diapause-like colorectal cancer cells induced by SMC4 attenuation are characterized by low proliferation and chemotherapy insensitivity.

Cell metabolism·2026
Same author

Deciphering the immunological landscape of HR + metastatic breast cancer: insights from single-cell transcriptomics.

Human cell·2026
Same author

Multi-granularity transformer contrastive learning and feature reconstruction for prediction of disease-related miRNAs.

BMC bioinformatics·2026
Same author

Towards a general-purpose foundation model for functional MRI analysis.

Nature biomedical engineering·2026
Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

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

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

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

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

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

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

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

Related Experiment Video

Updated: Jan 17, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.5K

Open Set Domain Adaptation via Known Joint Distribution Matching and Unknown Classification Risk Reformulation.

Sentao Chen, Ping Xuan, Lifang He

    IEEE Transactions on Neural Networks and Learning Systems
    |January 14, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Open set domain adaptation (OSDA) is addressed by the new KMUR approach, which matches known distributions and reformulates unknown risks for better machine learning models. This method improves classification accuracy by tackling source-target differences and unknown class challenges.

    Related Experiment Videos

    Last Updated: Jan 17, 2026

    An R-Based Landscape Validation of a Competing Risk Model
    05:37

    An R-Based Landscape Validation of a Competing Risk Model

    Published on: September 16, 2022

    2.5K

    Area of Science:

    • Machine Learning
    • Computer Vision
    • Statistical Learning

    Background:

    • Open Set Domain Adaptation (OSDA) addresses machine learning challenges with labeled source data and unlabeled target data containing known and unknown classes.
    • Existing OSDA methods struggle with source-target distribution differences and estimating risks for unknown classes.
    • Two key challenges in OSDA are the discrepancy between source and target joint distributions for known classes and estimating classification risk for unknown classes.

    Purpose of the Study:

    • Introduce a principled approach, Known Joint Distribution Matching and Unknown Classification Risk Reformulation (KMUR), to resolve key OSDA challenges.
    • Reduce source-target distribution differences by matching source and target known joint distributions.
    • Reformulate and estimate target unknown classification risk using unlabeled source and target data.

    Main Methods:

    • Employ cross-entropy for classification loss and Triangular Discrimination (TD) distance for joint distribution matching.
    • Develop Least Squares TD Estimation (LSTDE) to estimate TD distance by framing it as a least squares classification problem.
    • Train neural networks to minimize estimated target classification risk and TD distance for effective OSDA.

    Main Results:

    • KMUR effectively reduces source-target distribution differences and improves unknown class risk estimation.
    • Experimental results on benchmark and real-world datasets validate the proposed approach's effectiveness.
    • The method demonstrates significant improvements in open set domain adaptation tasks.

    Conclusions:

    • KMUR provides a robust framework for tackling the dual challenges of OSDA.
    • The approach offers a principled way to handle distribution shifts and unknown classes in domain adaptation.
    • The study contributes a novel method with practical implications for real-world machine learning applications.