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

7.9K
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...
7.9K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

157
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
157
Classification of Systems-II01:31

Classification of Systems-II

245
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,
245
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

130
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,...
130
Weighted Mean00:57

Weighted Mean

5.4K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
5.4K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

142
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....
142

You might also read

Related Articles

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

Sort by
Same author

Heterogeneous oblique double random forest.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

EDA-OCBLS: An error-distribution aware one-class broad learning system for anomaly detection.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

A comprehensive review of use cases, misuses, and potential mitigation techniques in generative artificial intelligence.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Dual-center RAPID-LSSVM: Radius-adaptive, probability and imbalance driven weighting for Alzheimer's diagnosis.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

A robust multi-view support vector machine with the RoBoSS loss function.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Fuzzy-driven broad learning system with class probability and density awareness for multi-view data.

Neural networks : the official journal of the International Neural Network Society·2026

Related Experiment Video

Updated: Sep 20, 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

Intuitionistic Fuzzy Weighted Least Squares Twin SVMs.

M Tanveer, M A Ganaie, A Bhattacharjee

    IEEE Transactions on Cybernetics
    |June 10, 2022
    PubMed
    Summary

    This study introduces a new intuitionistic fuzzy weighted least squares twin support vector machine (TWSVM) model. It improves classification accuracy by considering local data patterns and reducing noise, outperforming previous methods.

    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

    1.3K
    Asthma Detection Research Based on Voice Signal Processing and Machine Learning
    04:04

    Asthma Detection Research Based on Voice Signal Processing and Machine Learning

    Published on: July 22, 2025

    435

    Related Experiment Videos

    Last Updated: Sep 20, 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

    1.3K
    Asthma Detection Research Based on Voice Signal Processing and Machine Learning
    04:04

    Asthma Detection Research Based on Voice Signal Processing and Machine Learning

    Published on: July 22, 2025

    435

    Area of Science:

    • Machine Learning
    • Computational Intelligence
    • Data Mining

    Background:

    • Twin Support Vector Machines (TWSVMs) effectively handle classification but can be sensitive to noise and outliers.
    • Existing fuzzy TWSVM variants struggle to differentiate support vectors from noise and ignore local data structures.
    • Traditional TWSVM methods often involve computationally intensive quadratic programming problems.

    Purpose of the Study:

    • To propose a novel intuitionistic fuzzy weighted least squares TWSVM for enhanced classification.
    • To address the limitations of existing TWSVMs by incorporating local neighborhood information and robust outlier handling.
    • To develop a computationally efficient classification model by avoiding quadratic programming.

    Main Methods:

    • Developed an intuitionistic fuzzy weighted least squares TWSVM model.
    • Integrated local neighborhood information and dual membership (membership and nonmembership) weights.
    • Replaced quadratic programming with solving a system of linear equations for improved efficiency.

    Main Results:

    • The proposed model demonstrates superior efficiency and classification performance on benchmark datasets.
    • The method effectively reduces the impact of noise and outliers.
    • Statistical analysis confirmed the significant improvements achieved by the novel approach.

    Conclusions:

    • The intuitionistic fuzzy weighted least squares TWSVM offers a computationally efficient and robust alternative for classification tasks.
    • The model's ability to leverage local information and handle outliers makes it suitable for complex datasets.
    • The model shows promise for real-world applications, such as the diagnosis of Schizophrenia.