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

Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Gradient Vectors and Their Applications01:19

Gradient Vectors and Their Applications

Every point on a topographical map corresponds to a particular elevation, so the landscape can be modeled as a surface whose height depends on horizontal position. From any given location, a hiker may face infinitely many directions, but only one direction produces the fastest possible increase in elevation. This unique route is called the direction of steepest ascent, and in multivariable calculus, it is represented by the gradient vector of the elevation function.The gradient vector points...
Implicit Differentiation: Problem Solving01:29

Implicit Differentiation: Problem Solving

Curves defined implicitly, where variables cannot be separated algebraically, require specialized techniques for analysis. The conchoid of Nicomedes exemplifies such a case. Its equation links x and y in a way that prevents isolation of one variable, making implicit differentiation essential to determine the slope and behavior at any point on the curve.The implicit form of the conchoid can be expressed as:To differentiate this equation, y is treated as a function of x, and the chain rule is...
Gradient and Del Operator01:14

Gradient and Del Operator

In mathematics and physics, the gradient and del operator are fundamental concepts used to describe the behavior of functions and fields in space. The gradient is a mathematical operator that gives both the magnitude and direction of the maximum spatial rate of change. Consider a person standing on a mountain. The slope of the mountain at any given point is not defined unless it is quantified in a particular direction. For this reason, a "directional derivative" is defined, which is a vector...
Linear Approximations01:23

Linear Approximations

For a differentiable function of two variables, linear approximation estimates values near a known point by replacing the curved surface with its tangent plane. Consider the function\begin{equation*}f(x,y)=x^2+3y^2\end{equation*}near the point (2, 1). The exact value at this point is f(2, 1) = 22 + 3(1)2 = 4 + 3 = 7.The linear approximation of f(x, y)) near (a, b) is\begin{equation*}L(x,y)=f(a,b)+f_x(a,b)(x-a)+f_y(a,b)(y-b)\end{equation*}First, compute the partial derivatives: fx(x, y) = 2x and...

You might also read

Related Articles

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

Sort by
Same author

Variance-constrained multi-view ensemble broad network for imbalanced data.

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

Learning to Super-Resolve Face Images via Dual-Domain Multi-scale Feature Interaction.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Effectiveness of heterologous mRNA vaccine boosters during an Omicron wave of COVID-19: a cross-sectional study in Macao (China).

Journal of thoracic disease·2026
Same author

Fast BCIs: Leveraging Dual-Scale Time Windows with Test-Time Adaptation to Enhance Accuracy.

IEEE transactions on bio-medical engineering·2026
Same author

Riemannian Acceleration for Sparse PCA With Separable Structure and Second-Order Information Exploration.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Hierarchical memory-based deep reinforcement learning in simulated survival environments.

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

Related Experiment Videos

A gradient-descent-based approach for transparent linguistic interface generation in fuzzy models.

Long Chen1, C L Philip Chen, Witold Pedrycz

  • 1Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249-0669, USA. gbu922@my.utsa.edu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|December 8, 2009
PubMed
Summary

This study introduces GREETING, a novel approach for generating transparent linguistic interfaces. GREETING enhances fuzzy model interpretability and improves classification rates in fuzzy decision trees.

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Fuzzy Systems

Background:

  • Linguistic interfaces are crucial for fuzzy model interpretability.
  • Traditional methods often limit the consideration of interpretability.
  • Fuzzy model transparency directly impacts user understanding and trust.

Purpose of the Study:

  • To propose a novel approach for generating transparent linguistic interfaces.
  • To enhance the interpretability of fuzzy models.
  • To improve the performance of fuzzy decision trees.

Main Methods:

  • Developed the GRadiEnt-descEnt-based Transparent lInguistic iNterface Generation (GREETING) approach.
  • Incorporated and optimized widely used interpretability criteria.
  • Conducted numerical experiments on UCI machine learning databases.

Main Results:

  • Demonstrated the feasibility and superiority of the GREETING method.
  • GREETING significantly improves the transparency of linguistic interfaces.
  • Applied GREETING to fuzzy decision tree generation, yielding better classification rates.

Conclusions:

  • The GREETING approach offers a superior method for generating interpretable linguistic interfaces.
  • GREETING enhances fuzzy decision tree performance in terms of classification accuracy and tree size.
  • This method advances the field of transparent fuzzy modeling.