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...
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...
Sampling Distribution01:12

Sampling Distribution

Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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...

You might also read

Related Articles

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

Sort by
Same author

Modeling visual memorability assessment with autoencoders reveals characteristics of memorable images.

Scientific reports·2026
Same author

Controlling memorability of face images with generative models.

Scientific reports·2026
Same author

Distinct perceptual and conceptual representations of natural actions along the lateral and dorsal visual streams.

Communications biology·2026
Same author

Efficient slice anomaly detection network for 3D brain MRI Volume.

PLOS digital health·2025
Same author

Neural processing of naturalistic audiovisual events in space and time.

Communications biology·2025
Same author

Investigating Task-Free Functional Connectivity Patterns in Newborns Using Functional Near-Infrared Spectroscopy.

Brain and behavior·2024

Related Experiment Video

Updated: May 10, 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

The relevance sample-feature machine: a sparse Bayesian learning approach to joint feature-sample selection.

Yalda Mohsenzadeh, Hamid Sheikhzadeh, Ali M Reza

    IEEE Transactions on Cybernetics
    |June 21, 2013
    PubMed
    Summary
    This summary is machine-generated.

    This study presents a new machine learning algorithm, relevance sample feature machine (RSFM), for effective feature selection in classification. RSFM simultaneously identifies relevant features and data samples, improving accuracy and reducing complexity.

    Related Experiment Videos

    Last Updated: May 10, 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

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Statistical Learning

    Background:

    • Traditional machine learning algorithms often struggle with high-dimensional data and identifying truly relevant features.
    • Existing methods like the standard Relevance Vector Machine (RVM) achieve sparsity primarily in the sample domain.

    Purpose of the Study:

    • To introduce a novel sparse Bayesian machine learning algorithm for embedded feature and sample selection.
    • To enhance classification and regression tasks by simultaneously identifying relevant features and data samples.

    Main Methods:

    • Developed the Relevance Sample Feature Machine (RSFM), a novel algorithm extending the standard RVM.
    • Employed a separable model in feature and sample domains with a Bayesian approach and Gaussian priors.
    • Achieved sparsity in both feature and sample domains through the learned model.

    Main Results:

    • RSFM demonstrated success in feature selection by effectively eliminating irrelevant features.
    • The algorithm achieved accurate classification performance on synthetic and benchmark datasets.
    • Experimental results confirmed RSFM's ability to perform simultaneous feature and sample selection.

    Conclusions:

    • RSFM offers a robust solution for embedded feature selection in classification and regression.
    • The algorithm leads to reduced system complexity, improved generalization, and avoidance of overfitting.
    • RSFM presents a computationally efficient approach, particularly during the testing phase.