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

247
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...
247
Response Surface Methodology01:16

Response Surface Methodology

567
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
567
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

14.0K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
14.0K
Sampling Methods: Overview01:06

Sampling Methods: Overview

2.0K
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
2.0K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

5.0K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
5.0K
Cluster Sampling Method01:20

Cluster Sampling Method

13.9K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.9K

You might also read

Related Articles

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

Sort by
Same author

DAQplugin: Deep Learning based Real-time Model Evaluation Plugin for ChimeraX.

bioRxiv : the preprint server for biology·2026
Same author

Interfacial Redox Decoupling via Amphiphilic Carbon Dots for Highly Efficient Biphasic Photocatalytic H<sub>2</sub>O<sub>2</sub> Production and Selective Benzyl Alcohol Oxidation.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

Beta-Adrenergic Stimulation and <i>MYH7</i> G256E Mutant Gene Dosage Drive Hypertrophic Cardiomyopathy Phenotype Penetrance.

bioRxiv : the preprint server for biology·2026
Same author

Stress-Driven Accelerated Evolution and Ecological Network Reconfiguration in Extremophilic Microbial Communities.

Biology·2026
Same author

Workload-induced changes to cell state contribute to β-cell failure in diabetes.

bioRxiv : the preprint server for biology·2026
Same author

A nomogram estimates postoperative urinary tract infection risk after upper urinary tract stone surgery with double-J stents.

Scientific reports·2026
Same journal

An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models.

IEEE transactions on cybernetics·2026
Same journal

A Quantum Self-Attention Neural Network Model on Quantum Circuits.

IEEE transactions on cybernetics·2026
Same journal

Semi-Explicit Solution of Some Discrete-Time Higher-Order-Cost Mean-Field-Type Control.

IEEE transactions on cybernetics·2026
Same journal

A Novel One-Step Small Object Detector for Autonomous Aerial Vehicles.

IEEE transactions on cybernetics·2026
Same journal

Online Data-Driven-Based Optimal Output Tracking Control Without Initial Stabilizing Policy.

IEEE transactions on cybernetics·2026
Same journal

Digital Redesign-Based Interval State Estimation for Continuous Systems With Aperiodic Discrete Measurements.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: Jan 3, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K

A Survey of Optimization Methods From a Machine Learning Perspective.

Shiliang Sun, Zehui Cao, Han Zhu

    IEEE Transactions on Cybernetics
    |November 22, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This review summarizes machine learning optimization methods, detailing common techniques and their advancements. It highlights current challenges and future research directions in this rapidly evolving field.

    Related Experiment Videos

    Last Updated: Jan 3, 2026

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    7.9K

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Machine learning is rapidly advancing with significant theoretical breakthroughs and broad applications.
    • Optimization is a critical component of machine learning, attracting substantial research interest.
    • Increasing data volume and model complexity present significant challenges for machine learning optimization.

    Purpose of the Study:

    • To provide a systematic review and summary of optimization methods in machine learning.
    • To offer guidance for future research and development in both optimization and machine learning.
    • To identify current challenges and open problems in machine learning optimization.

    Main Methods:

    • Describing the fundamental optimization problems encountered in machine learning.
    • Introducing the principles and recent progress of widely used optimization algorithms.
    • Exploring existing literature on optimization techniques applied to machine learning models.

    Main Results:

    • A comprehensive overview of machine learning optimization problems.
    • Detailed explanations of the principles and advancements of common optimization methods.
    • Identification of key challenges and open research questions in the field.

    Conclusions:

    • Optimization is crucial for the advancement of machine learning.
    • Continued research is needed to address the evolving challenges in machine learning optimization.
    • This review serves as a valuable resource for researchers in machine learning and optimization.