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

Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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...

You might also read

Related Articles

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

Sort by
Same author

Genome-wide identification and expression analysis of the <i>CA</i> gene family in brown algae <i>Saccharina sculpera</i>.

Frontiers in plant science·2026
Same author

Chromosome-scale genome assembly and annotation of Saccharina sculpera.

Scientific data·2025
Same author

Employees' perception of digital human resource management changes and proactive behavior: the mediating role of work engagement and moderating effect of person-organization fit.

Frontiers in psychology·2025
Same author

Damage and immunosuppression to Mytilus galloprovincialis hemocytes caused by chronic tris(chloropropyl)phosphate exposure.

Ecotoxicology and environmental safety·2025
Same author

Heterogeneity of Cough Hypersensitivity Induced by Mechanical and Chemical Stimulation in Patients With Chronic Cough.

Archivos de bronconeumologia·2025
Same author

Occurrence, spatial distributions, sources, and potential risks of organophosphate esters in Yarlung Tsangpo River and its main tributaries on the Tibetan Plateau.

Environmental pollution (Barking, Essex : 1987)·2025

Related Experiment Video

Updated: Jul 12, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)
19:44

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

Gap-based estimation: choosing the smoothing parameters for probabilistic and general regression neural networks.

Mingyu Zhong1, Dave Coggeshall, Ehsan Ghaneie

  • 1School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA. myzhong@ucf.edu

Neural Computation
|August 25, 2007
PubMed
Summary

This study introduces a fast gap-based estimation approach for optimizing the smoothing parameter in Probabilistic Neural Networks (PNN) and General Regression Neural Networks (GRNN), outperforming traditional methods in speed and accuracy.

Related Experiment Videos

Last Updated: Jul 12, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)
19:44

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Probabilistic neural networks (PNN) and general regression neural networks (GRNN) are interpretable models for classification and prediction.
  • These models rely on a crucial smoothing parameter, typically selected via cross-validation or clustering.

Purpose of the Study:

  • To highlight the limitations of cross-validation and clustering for smoothing parameter selection.
  • To propose a novel, fast gap-based estimation approach for determining the optimal smoothing parameter.
  • To analyze the relationship between the smoothing parameter and data statistics.

Main Methods:

  • Investigated problems with existing cross-validation and clustering methods.
  • Developed a new gap-based estimation approach for smoothing parameter selection.
  • Conducted experiments to compare the new approach with existing methods, including Support Vector Machines.

Main Results:

  • The gap-based estimation approach demonstrates superior speed compared to cross-validation, clustering, and Support Vector Machines.
  • The proposed method achieves good and stable accuracy in PNN and GRNN models.
  • Identified relationships between the smoothing parameter and data statistics.

Conclusions:

  • The gap-based estimation approach offers a significantly faster and effective alternative for tuning PNN and GRNN models.
  • This method provides a reliable way to achieve optimal performance for these neural network architectures.
  • The findings suggest a more efficient pathway for applying PNN and GRNN in practical applications.