Jove
Visualize
Contact Us

Related Concept Videos

Conservative Vector Fields01:29

Conservative Vector Fields

A conservative vector field describes a force or field in which the work done between two points depends only on the initial and final positions. For a ball moving in Earth’s gravitational field, gravity performs work determined by the difference in height, regardless of whether the ball moves vertically or follows a curved trajectory.A vector field is conservative if it can be expressed as the gradient of a scalar potential function, f. In two dimensions, this is written...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

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...
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...
Regression Analysis01:11

Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Regression Toward the Mean01:52

Regression Toward the Mean

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 researchers try to extrapolate results...
Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

You might also read

Related Articles

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

Sort by
Same author

Radial basis function networks with linear interval regression weights for symbolic interval data.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2011
Same author

An approximation of interval type-2 fuzzy controllers using fuzzy ratio switching type-1 fuzzy controllers.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2010
Same author

Robust support vector regression networks for function approximation with outliers.

IEEE transactions on neural networks·2008
Same author

Hybrid compensation control for affine TSK fuzzy control systems.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2004
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 Videos

Fuzzy weighted support vector regression with a fuzzy partition.

Chen-Chia Chuang1

  • 1Department of Electrical Engineering, National Ilan University, I-Lan 260, Taiwan, ROC. ccchuang@niu.edu.tw

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|June 7, 2007
PubMed
Summary

A novel fuzzy weighted Support Vector Regression (SVR) approach effectively models local data behavior without boundary effects. This method improves accuracy and reduces computation time compared to traditional global and local SVR techniques.

Related Experiment Videos

Area of Science:

  • Machine Learning
  • Computational Statistics

Background:

  • Traditional global Support Vector Regression (SVR) struggles with local model behavior interpretation.
  • Local SVR improves local modeling but suffers from boundary effects and increased computation time.

Purpose of the Study:

  • To propose a fuzzy weighted SVR approach that overcomes the limitations of global and local SVR methods.
  • To enhance model accuracy and reduce computational cost in regression tasks.

Main Methods:

  • Utilizes fuzzy c-mean clustering to partition training data into subsets.
  • Independently generates local-regression models (LRMs) for each subset using SVR.
  • Combines LRMs via a fuzzy weighted mechanism to produce the final output, avoiding locally weighted regression.

Main Results:

  • The proposed fuzzy weighted SVR approach eliminates boundary effects inherent in local SVR.
  • Achieves more accurate results compared to both local and global SVR approaches.
  • Demonstrates reduced computational time relative to the local SVR approach.

Conclusions:

  • The fuzzy weighted SVR with fuzzy partition offers a superior alternative for regression tasks requiring local behavior modeling.
  • This method provides a balance of accuracy and computational efficiency.