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

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...
Linearization and Approximation01:26

Linearization and Approximation

Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
Newton’s Method01:30

Newton’s Method

Newton’s Method is a powerful iterative technique for approximating the roots of real-valued, differentiable functions, particularly when analytical solutions are impractical. This approach is widely used in scientific computing, engineering, and finance, where equations may be too complex for traditional algebraic methods to handle. The method relies on an iterative process that refines an initial estimate using the function’s derivative to approach the true solution progressively.
Approximate Integration01:24

Approximate Integration

In many practical and theoretical contexts, the exact value of a definite integral may be inaccessible. This limitation typically arises when the antiderivative of a function is either unknown or cannot be expressed in a closed mathematical form. Alternatively, it can occur when a function is defined not by a formula but by a finite set of empirical data points, such as those collected during experiments. In these cases, approximate integration techniques provide a valuable solution.One of the...
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

A comprehensive study on enrofloxacin residue depletion in five chicken breeds.

Poultry science·2026
Same author

Polystyrene Nanoplastics Exposure Alters Gut Microbiota and Correlates with Egg Quality Parameters in Chickens.

Animals : an open access journal from MDPI·2025
Same author

Separation, characterization, AI screening, and bioactivities of marine bioactive peptides: A review.

Food chemistry·2025
Same author

Untargeted Metabolomics Uncovers Food Safety Risks: Polystyrene Nanoplastics Induce Metabolic Disorders in Chicken Liver.

Foods (Basel, Switzerland)·2025
Same author

Associative knowledge graphs for efficient sequence storage and retrieval.

Computer methods and programs in biomedicine·2025
Same author

The RAE1-STOP1 module regulates ABA sensitivity in early seedlings of Arabidopsis.

BMC plant biology·2025
Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013
Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011
Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011
Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011
Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011
Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011
See all related articles

Related Experiment Video

Updated: Jun 30, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Optimized approximation algorithm in neural networks without overfitting.

Yinyin Liu1, Janusz A Starzyk, Zhen Zhu

  • 1School of Electrical Engineering and Computer Science, Ohio University, Athens, OH 45701, USA. yliu@bobcat.ent.ohiou.edu

IEEE Transactions on Neural Networks
|June 11, 2008
PubMed
Summary
This summary is machine-generated.

An optimized approximation algorithm (OAA) prevents neural network overfitting using a novel signal-to-noise-ratio figure (SNRF) stopping criterion. This method detects overfitting from training error alone, eliminating the need for validation sets.

Related Experiment Videos

Last Updated: Jun 30, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computational Science

Background:

  • Overfitting is a significant challenge in neural network (NN) function approximation.
  • Traditional methods often require separate validation sets to detect overfitting.
  • Efficiently optimizing NN parameters remains an active research area.

Purpose of the Study:

  • To introduce an optimized approximation algorithm (OAA) to mitigate overfitting in NNs.
  • To present a novel stopping criterion based on the signal-to-noise-ratio figure (SNRF) for automatic overfitting detection.
  • To demonstrate the algorithm's utility in optimizing NN architectures and training parameters.

Main Methods:

  • Developed an optimized approximation algorithm (OAA) incorporating a signal-to-noise-ratio figure (SNRF) for goodness-of-fit estimation.
  • Implemented SNRF as a stopping criterion to detect overfitting directly from training error.
  • Applied OAA to optimize hidden neuron count and learning epochs in Multilayer Perceptrons (MLPs).

Main Results:

  • The OAA effectively detects and avoids overfitting without requiring a separate validation set.
  • SNRF accurately estimates the goodness-of-fit, enabling automatic overfitting identification.
  • The algorithm successfully optimized MLP parameters using both synthetic and benchmark datasets.

Conclusions:

  • The proposed OAA offers an effective solution for preventing overfitting in neural network function approximation.
  • The SNRF criterion provides a robust and efficient method for model selection and parameter optimization.
  • OAA is broadly applicable to various NN architectures, basis functions, and model selection problems where overfitting is a concern.