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

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
Scalar and Vector Triple Products01:06

Scalar and Vector Triple Products

2.5K
Two vectors can be multiplied using a scalar product or a vector product. The resultant of a scalar product is scalar, while with vector products, the resultant is a vector. These rules of the scalar or vector product between two vectors can be applied to multiple vectors to obtain meaningful combinations. The scalar triple product is the dot product of a vector with the cross product of two vectors.
The scalar triple product is the dot product of a vector with the cross product of two vectors....
2.5K
Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

295
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
295
Vector Components in the Cartesian Coordinate System01:29

Vector Components in the Cartesian Coordinate System

20.2K
Vectors are usually described in terms of their components in a coordinate system. Even in everyday life, we naturally invoke the concept of orthogonal projections in a rectangular coordinate system. For example, if someone gives you directions for a particular location, you will be told to go a few km in a direction like east, west, north, or south, along with the angle in which you are supposed to move. In a rectangular (Cartesian) xy-coordinate system in a plane, a point in a plane is...
20.2K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.4K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
1.4K
Routh-Hurwitz Criterion I01:15

Routh-Hurwitz Criterion I

280
Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
280

You might also read

Related Articles

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

Sort by
Same author

Kernel-Free Quadratic Surface Regression for Multi-Class Classification.

Entropy (Basel, Switzerland)·2023
Same author

Collapse of hybrid vector beam in Rb atomic vapor.

Optics letters·2021
Same author

Self-Treatment of Posterior Canal Benign Paroxysmal Positional Vertigo: A Preliminary Study.

Frontiers in medicine·2021
Same author

GCN2 Regulates ATF3-p38 MAPK Signaling Transduction in Pulmonary Veno-Occlusive Disease.

Journal of cardiovascular pharmacology and therapeutics·2021
Same author

Serum Levels of ITGBL1 as an Early Diagnostic Biomarker for Hepatocellular Carcinoma with Hepatitis B Virus Infection.

Journal of hepatocellular carcinoma·2021
Same author

Mixotrophic Chlorella pyrenoidosa as cell factory for ultrahigh-efficient removal of ammonium from catalyzer wastewater with valuable algal biomass coproduction through short-time acclimation.

Bioresource technology·2021
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jul 21, 2025

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

9.3K

Kernel-Free Quadratic Surface Support Vector Regression with Non-Negative Constraints.

Dong Wei1,2, Zhixia Yang1,2, Junyou Ye1,2

  • 1College of Mathematics and Systems Science, Xinjiang University, Urumuqi 830046, China.

Entropy (Basel, Switzerland)
|July 29, 2023
PubMed
Summary
This summary is machine-generated.

A new kernel-free quadratic support vector regression with non-negative constraints (NQSSVR) offers interpretable regression models. This approach ensures monotonic increases, validated by experiments and real-world air quality data.

Keywords:
air quality composite index datasetkernel-freenon-negative constraintsquadratic surfaceregression problem

More Related Videos

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

1.2K
Surface Mapping of Earth-like Exoplanets using Single Point Light Curves
06:48

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

Published on: May 10, 2020

3.6K

Related Experiment Videos

Last Updated: Jul 21, 2025

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

9.3K
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

1.2K
Surface Mapping of Earth-like Exoplanets using Single Point Light Curves
06:48

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

Published on: May 10, 2020

3.6K

Area of Science:

  • Machine Learning
  • Regression Analysis
  • Data Science

Background:

  • Regression problems often require complex models.
  • Choosing kernel functions and parameters can be challenging.
  • A priori information about data monotonicity is often available.

Purpose of the Study:

  • To propose a kernel-free quadratic surface support vector regression with non-negative constraints (NQSSVR).
  • To develop a regression model that is interpretable and handles monotonic data.
  • To ensure the regression function aligns with prior knowledge of increasing trends.

Main Methods:

  • Utilized a quadratic surface kernel-free technique for regression.
  • Introduced non-negative constraints on regression coefficients.
  • Constructed an optimization problem for NQSSVR.
  • Addressed the existence, uniqueness, and relationship between primal and dual problems.

Main Results:

  • The NQSSVR model provides an interpretable quadratic regression function.
  • Theoretical analysis confirmed the regression function matches a priori information.
  • Experimental results on artificial and benchmark datasets demonstrated feasibility and effectiveness.
  • Validated the method using real-world air quality data.

Conclusions:

  • NQSSVR is an effective and interpretable method for regression problems with monotonic trends.
  • The kernel-free approach simplifies model selection and enhances understanding.
  • The method shows promise for applications like environmental data analysis.