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

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

241
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
241
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

289
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
289
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

407
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
407
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

623
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
623
Improving Translational Accuracy02:07

Improving Translational Accuracy

13.5K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
13.5K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.4K
3.4K

You might also read

Related Articles

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

Sort by
Same author

Natural variations in GhNF-YB3 contribute to seed cotton yield by modulating source-to-sink sucrose allocation.

The Plant cell·2026
Same author

FX-Cell: a method for single-cell RNA sequencing on difficult-to-digest and cryopreserved plant samples.

Nature methods·2025
Same author

Linear convergence of proximal gradient method for linear sparse SVM.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Local signal detection on irregular domains with generalized varying coefficient models.

Journal of the American Statistical Association·2025
Same author

A unified cell atlas of vascular plants reveals cell-type foundational genes and accelerates gene discovery.

Cell·2025
Same author

Decentralized Nonconvex Low-rank Matrix Recovery.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
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
Same journal

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

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

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

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

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

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

Related Experiment Video

Updated: Dec 11, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.5K

Learning Rate for Convex Support Tensor Machines.

Heng Lian

    IEEE Transactions on Neural Networks and Learning Systems
    |August 25, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study extends tensor regression for prediction problems to classification tasks using a hinge loss. It establishes statistical properties, showing rates depend on tensor dimension and predictor complexity.

    More Related Videos

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.8K
    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
    07:05

    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

    Published on: October 27, 2016

    9.5K

    Related Experiment Videos

    Last Updated: Dec 11, 2025

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
    08:27

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

    Published on: January 5, 2024

    1.5K
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.8K
    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
    07:05

    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

    Published on: October 27, 2016

    9.5K

    Area of Science:

    • Machine Learning
    • Statistical Modeling
    • High-Dimensional Data Analysis

    Background:

    • Tensors are increasingly utilized in complex prediction tasks.
    • Existing methods primarily focus on regression with least-squares loss.
    • Extending these methods to classification is a significant challenge.

    Purpose of the Study:

    • To adapt high-dimensional convex tensor regression for classification problems using a hinge loss.
    • To analyze the asymptotic statistical properties of the proposed classification method.
    • To identify key factors influencing the performance of tensor-based classification.

    Main Methods:

    • Extension of convex tensor regression techniques to handle hinge loss in classification.
    • Application of general convex decomposable penalties.
    • Asymptotic analysis of statistical properties.

    Main Results:

    • The study establishes the asymptotic statistical properties for tensor-based classification with hinge loss.
    • The derived prediction rate is shown to depend on the intrinsic dimension of the tensor predictors.
    • Rademacher complexity of the linear function class also significantly impacts the rate.

    Conclusions:

    • The proposed method provides a robust framework for high-dimensional tensor classification.
    • Understanding the interplay between tensor dimension and data complexity is crucial for effective prediction.
    • This work bridges a gap in applying advanced tensor methods to classification problems.