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

Sampling Methods: Overview01:06

Sampling Methods: Overview

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 sampling...
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
Random Sampling Method01:09

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures 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. Among the various sampling methods used by...
Systematic Sampling Method01:17

Systematic Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures 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.
Systematic sampling is one of the simplest methods...
Cluster Sampling Method01:20

Cluster Sampling Method

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...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...

You might also read

Related Articles

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

Sort by
Same author

Detection and Characterization of ESBL-Producing and Carbapenem-Resistant Klebsiella pneumoniae in Ornamental Birds and Their Surrounding Environments.

MicrobiologyOpen·2026
Same author

The consequences of migration and displacement for mental health: A qualitative study of river nomadic communities.

Global mental health (Cambridge, England)·2026
Same author

Relationship between hygienic management practices of smallholder dairy farms and the distribution of Gram-negative mastitis pathogens along with their antibiogram in Bangladesh.

Veterinary and animal science·2026
Same author

In silico characterization of bioactive phytochemicals as antivirals targeting the reovirus σ1 protein for inhibiting σ1-mediated host cell entry.

PloS one·2026
Same author

Factors associated with scabies severity and reinfection: A cross-sectional study during recent surges in the Chattogram Division, Bangladesh.

IJID regions·2026
Same author

Differences in Perspectives and Policies Regarding End-of-Life Care Between Hematologists and Gastroenterologists.

Cancer medicine·2026

Related Experiment Video

Updated: Jun 22, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

A comparative study of data sampling techniques for constructing neural network ensembles.

M A H Akhand1, Md Monirul Islam, Kazuyuki Murase

  • 1Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan. arkhand@cse.kuet.ac.jp

International Journal of Neural Systems
|June 5, 2009
PubMed
Summary
This summary is machine-generated.

Ensemble methods improve classifier performance through diversity. Negative correlation learning rivals bagging and boosting in effectiveness for neural network ensembles, offering a competitive approach to enhancing generalization.

Related Experiment Videos

Last Updated: Jun 22, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computer Science

Background:

  • Ensemble methods enhance classifier generalization by combining multiple models.
  • Classifier diversity is crucial for ensemble performance, enabling error compensation.
  • Data sampling techniques are effective for creating diverse classifiers in ensembles.

Purpose of the Study:

  • To investigate and experimentally evaluate prominent data sampling techniques for neural network ensembles.
  • To analyze the relationship between generalization and diversity in ensemble learning.
  • To compare the effectiveness of various ensemble methods on benchmark classification problems.

Main Methods:

  • Studied prominent data sampling techniques for neural network ensembles.
  • Experimentally evaluated eight ensemble methods on 30 benchmark classification problems.
  • Analyzed generalization and diversity using overlap and uncover metrics.

Main Results:

  • Bagging and boosting remain highly effective ensemble methods.
  • Negative correlation learning demonstrates comparable or superior performance to bagging and boosting.
  • Implicitly encouraging diverse training spaces via negative correlation learning is effective.

Conclusions:

  • Negative correlation learning offers a promising alternative to traditional ensemble methods like bagging and boosting.
  • Data sampling techniques are vital for constructing effective neural network ensembles.
  • Achieving classifier diversity is key to improving ensemble generalization performance.