Jove
Visualize
Contact Us

Related Concept Videos

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

9.9K
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...
9.9K
Quadratic Models01:23

Quadratic Models

336
Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
336
Gaussian Elimination: Problem Solving01:30

Gaussian Elimination: Problem Solving

281
Systems of linear equations in several variables are pivotal in modeling complex scenarios involving multiple unknowns and constraints. Such systems are widely used in various fields to represent relationships where several conditions must be simultaneously satisfied. Each variable in the system corresponds to an unknown quantity, while each equation imposes a linear constraint, leading to a structured approach for analyzing and solving real-world problems.A system of three equations with three...
281
Regression Toward the Mean01:52

Regression Toward the Mean

7.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
7.3K
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

181
A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
181
Separable Differential Equations01:20

Separable Differential Equations

303
A separable differential equation is a type of first-order differential equation where the derivative dy/dx can be expressed as a product of two functions: one that depends only on x and another that depends only on y. This allows for the rearrangement of the equation so that all terms involving y are on one side, and all terms involving x are on the other. This process, known as the separation of variables, simplifies the process of solving the equation by enabling the integration of both...
303

You might also read

Related Articles

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

Sort by
Same author

A Generalized Time Rescaling Theorem for Temporal Point Processes.

Neural computation·2025
Same author

A multi-firearm, multi-orientation audio dataset of gunshots.

Data in brief·2023
Same author

Pareto-Optimal Model Selection via SPRINT-Race.

IEEE transactions on cybernetics·2017
Same author

Multi-Objective Model Selection via Racing.

IEEE transactions on cybernetics·2015
Same author

Pareto-path multitask multiple kernel learning.

IEEE transactions on neural networks and learning systems·2014
Same author

Multitask Classification Hypothesis Space With Improved Generalization Bounds.

IEEE transactions on neural networks and learning systems·2014
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
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 Experiment Video

Updated: Apr 11, 2026

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

1.8K

A Simple Method for Solving the SVM Regularization Path for Semidefinite Kernels.

Christopher G Sentelle, Georgios C Anagnostopoulos, Michael Georgiopoulos

    IEEE Transactions on Neural Networks and Learning Systems
    |May 27, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel path-following algorithm for Support Vector Machines (SVMs) that efficiently handles semidefinite kernels. The new method offers competitive training times and high accuracy, improving upon existing algorithms.

    More Related Videos

    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

    9.8K
    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    20.7K

    Related Experiment Videos

    Last Updated: Apr 11, 2026

    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

    1.8K
    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

    9.8K
    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    20.7K

    Area of Science:

    • Machine Learning
    • Computational Statistics

    Background:

    • Support Vector Machines (SVMs) are popular classifiers but require regularization parameter tuning.
    • Existing regularization path algorithms like SVMPath and ISVMP have limitations, particularly with semidefinite kernels and computational requirements.

    Purpose of the Study:

    • To develop a simple, efficient path-following algorithm for SVMs that automatically handles semidefinite kernels.
    • To address limitations of previous methods regarding computational complexity and specialized solvers.

    Main Methods:

    • A novel implementation of a path-following algorithm for SVMs.
    • Theoretical analysis of semidefinite kernel handling, degeneracy, and cycling resolution.
    • An initialization method for unequal class sizes using artificial variables.

    Main Results:

    • The proposed algorithm effectively handles semidefinite kernels without specialized factorizations or external solvers.
    • Demonstrated resolution of degeneracy and cycling issues.
    • Experimental results show competitive training times and high accuracy compared to the ISVMP algorithm.

    Conclusions:

    • The new path-following algorithm provides an efficient and robust solution for SVM optimization, especially with semidefinite kernels.
    • The method is a viable alternative to existing algorithms, offering improved simplicity and performance.