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

Survival Tree01:19

Survival Tree

138
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
138
Reducing Line Loss01:18

Reducing Line Loss

188
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
188
Line Loss01:10

Line Loss

288
The different configurations of source-load connections include wye (star) and delta connections. The relationship between line and phase voltages and currents varies depending on the configuration. When the source is supplying power, it is transmitted through the wires to the load, and during this transmission, some power is absorbed by the wires, leading to line loss.
Line loss impacts power delivery efficiency in a balanced three-phase circuit. The symmetry in such a circuit simplifies the...
288
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

962
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...
962
Protein Networks02:26

Protein Networks

4.1K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.1K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

777
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
777

You might also read

Related Articles

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

Sort by
Same author

Flexible Prescribed-Time Optimal Control With Adaptive State-Input Constraint Bounds via Actor-Critic Learning.

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

Toward Comprehensive Information-Theoretic Multi-View Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Polystyrene nanoplastics facilitate the Fe(II)-catalyzed ferrihydrite transformation into goethite and hematite under sedimentary conditions.

Journal of hazardous materials·2026
Same author

Integrative effects of irrigation and aeration on root morphology, yield, and quality of tomatoes cultivated in coastal saline-alkali lands.

Scientific reports·2026
Same author

Functional connectivity-based classification and subtyping of major depression for precision mental health: An ensemble graph neural network approach.

PLOS digital health·2026
Same author

DAMind: Zero-Shot Visual Cross-Domain Alignment and Representation for EEG Decoding.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·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: Aug 29, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Error Loss Networks.

Badong Chen, Yunfei Zheng, Pengju Ren

    IEEE Transactions on Neural Networks and Learning Systems
    |September 13, 2022
    PubMed
    Summary
    This summary is machine-generated.

    A novel error loss network (ELN) creates adaptable loss functions for supervised learning. This new machine learning approach enhances model performance by learning the loss function before the main learning phase.

    Related Experiment Videos

    Last Updated: Aug 29, 2025

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
    07:15

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

    Published on: August 16, 2020

    6.9K

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Neural Networks

    Background:

    • Supervised learning relies on well-defined loss functions.
    • Existing loss functions may not be optimal for all tasks.
    • Information-theoretic learning (ITL) offers advanced loss function concepts.

    Purpose of the Study:

    • To introduce a novel Error Loss Network (ELN) for constructing adaptable error loss functions.
    • To propose a new two-stage machine learning paradigm.
    • To demonstrate the effectiveness of the ELN model.

    Main Methods:

    • Developed an Error Loss Network (ELN) inspired by Radial Basis Function (RBF) networks.
    • Designed the ELN to map error samples to corresponding loss values.
    • Implemented a two-stage learning process: first, learning the loss function with ELN, then performing supervised learning.

    Main Results:

    • The ELN provides a unified model for various error loss functions, including ITL losses.
    • ELN's parameters (activation function, weights, network size) can be predefined or learned.
    • Experimental results validate the superior performance of the proposed ELN-based method.

    Conclusions:

    • The ELN offers a flexible and powerful approach to designing error loss functions.
    • The proposed two-stage learning paradigm enhances supervised learning outcomes.
    • ELN represents a significant advancement in machine learning methodology.