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Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Random Variables01:09

Random Variables

A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
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...
Types of Selection01:46

Types of Selection

Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...

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Related Experiment Video

Updated: May 27, 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

Stochastic subset selection for learning with kernel machines.

Jason Rhinelander1, Xiaoping P Liu

  • 1Department of Systems and Computer Engineering, Faculty of Engineering and Design, Carleton University, Ottawa, ON, Canada. jasonr@sce.carleton.ca

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|November 4, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel subset selection method for kernel machines, enhancing online learning efficiency. The algorithm scales linearly and improves recognition accuracy, outperforming standard techniques in real-time environments.

Related Experiment Videos

Last Updated: May 27, 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
  • Computational Statistics

Background:

  • Kernel machines, including Support Vector Machines (SVMs), are widely used in machine learning for various tasks.
  • Traditional SVM training involves computationally intensive quadratic programming (QP) problems, limiting their use in real-time applications.
  • Online kernel machines require computationally efficient methods to handle streaming data.

Purpose of the Study:

  • To develop a computationally efficient subset selection method for kernel machines suitable for online, changing environments.
  • To improve the scalability and real-time performance of kernel machines in machine learning applications.
  • To enhance recognition accuracy in online learning scenarios.

Main Methods:

  • Introduced a novel subset selection algorithm for kernel machines using stochastic indexing.
  • Separated the selection of kernel basis functions from the online training algorithm.
  • The algorithm scales linearly with the number of training samples.

Main Results:

  • The proposed algorithm achieves linear scalability with the number of training samples, outperforming standard super-linear methods.
  • Demonstrated increased recognition accuracy compared to existing techniques in experimental evaluations.
  • The method is compatible with various online training techniques.

Conclusions:

  • The developed subset selection method significantly enhances the computational efficiency of online kernel machines.
  • The algorithm offers a practical solution for real-time machine learning applications requiring efficient data processing.
  • Experimental results validate the algorithm's effectiveness on both simulated and real-world datasets.