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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

38
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
38
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

620
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
620
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

450
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
450
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.2K
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...
7.2K
The Uncertainty Principle04:08

The Uncertainty Principle

22.9K
Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
22.9K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

23
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
23

You might also read

Related Articles

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

Sort by
Same author

VirBinn improves viral genome binning from metagenomic Hi-C through graph diffusion.

Bioinformatics (Oxford, England)·2026
Same author

Arabidopsis PP2C clade B members are negative feedback regulators of MPK3/MPK6 MAPK cascade in plant immunity and development.

Plant physiology·2026
Same author

The Kelch-Repeat Superfamily Gene <i>SiNL4</i> Regulates the Leaf Width in Foxtail Millet.

Plants (Basel, Switzerland)·2026
Same author

Substrate affinity and spatial proximity synergistically guide multi-enzyme architecture rewiring for highly efficient chitin conversion.

Bioresource technology·2026
Same author

Transcription factor ID3 promotes fibroblast differentiation and proliferation in lung fibrosis through augmenting the TGF-β signaling pathway.

Molecular immunology·2026
Same author

ChatCLIDS: Simulating Persuasive AI Dialogues to Promote Closed-Loop Insulin Adoption in Type 1 Diabetes Care.

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence·2026

Related Experiment Video

Updated: May 23, 2025

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
11:22

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

10.0K

Complex quantized minimum error entropy with fiducial points: theory and application in model regression.

Bingqing Lin1, Guobing Qian1, Zongli Ruan2

  • 1College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.

Neural Networks : the Official Journal of the International Neural Network Society
|March 11, 2025
PubMed
Summary

Minimum error entropy with fiducial points (MEEF) is computationally intensive. A new complex QMEEF method improves efficiency and accuracy for noise-corrupted regression tasks, outperforming existing techniques.

Keywords:
Complex domainConvergenceFixed-point algorithmLinear-in-parameters (LIP) modelQuantized techniqueRegression analysis

More Related Videos

A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference
00:07

A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference

Published on: September 5, 2019

8.4K
A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

9.8K

Related Experiment Videos

Last Updated: May 23, 2025

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
11:22

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

10.0K
A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference
00:07

A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference

Published on: September 5, 2019

8.4K
A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

9.8K

Area of Science:

  • Machine Learning
  • Signal Processing

Background:

  • Minimum error entropy with fiducial points (MEEF) effectively reduces non-Gaussian noise.
  • The original MEEF algorithm has high computational complexity.
  • Quantized MEEF (QMEEF) offers improved efficiency via quantization.

Purpose of the Study:

  • To introduce complex QMEEF (CQMEEF) for enhanced noise mitigation in the complex domain.
  • To theoretically analyze the properties and convergence of CQMEEF.
  • To evaluate CQMEEF's performance in training Linear-in-parameters (LIP) models.

Main Methods:

  • Extension of QMEEF techniques to the complex domain.
  • Theoretical analysis of CQMEEF properties and convergence.
  • Application of CQMEEF to train LIP models on noise-corrupted datasets.

Main Results:

  • CQMEEF demonstrates theoretical convergence and fundamental properties.
  • CQMEEF achieves high precision in regression tasks with noisy data.
  • CQMEEF outperforms existing methods in critical performance metrics.

Conclusions:

  • CQMEEF provides an efficient computational alternative for complex data regression.
  • CQMEEF offers a novel approach for handling noise-corrupted complex datasets.
  • CQMEEF shows broad applicability in machine learning and signal processing.