Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Fisher's Exact Test

Fisher's Exact Test

Fisher's exact test is a statistical significance test widely used to analyze 2x2 contingency tables, particularly in situations where sample sizes are small. Unlike the chi-squared test, which approximates P-values and assumes minimum expected frequencies of at least five in each cell, Fisher's exact test calculates the exact probability (P-value) of observing the data or more extreme results under the null hypothesis. This feature makes it especially valuable when the assumptions of the...

Behrens–Fisher Test

Behrens–Fisher Test

The Behrens-Fisher test is a statistical method designed to address the Behrens-Fisher problem, which arises when comparing the means of two normally distributed populations with unequal variances. Unlike the Student's t-test, which assumes equal variances, the Behrens-Fisher test allows for mean comparison without this restrictive assumption. This flexibility makes it particularly valuable in scenarios where two independent samples exhibit normality but lack variance homogeneity.
This test is...

You might also read

Related Articles

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

Sort by

Same author

[Safety study of navigation-assisted medial "in-out-in" technique in C<sub>2</sub> screw fixation].

Zhonghua wai ke za zhi [Chinese journal of surgery]·2025

Same author

Improving robustness of gene ranking by multi-criterion combination with novel gene importance transformation.

International journal of data mining and bioinformatics·2013

Same author

Robust feature selection for microarray data based on multicriterion fusion.

IEEE/ACM transactions on computational biology and bioinformatics·2011

Same author

Recursive Mahalanobis separability measure for gene subset selection.

IEEE/ACM transactions on computational biology and bioinformatics·2010

Same author

Probabilistic neural-network structure determination for pattern classification.

IEEE transactions on neural networks·2008

Same author

Fast orthogonal forward selection algorithm for feature subset selection.

IEEE transactions on neural networks·2008

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

Anesthesia-free Heartbeat Measurements in Freely Moving Zebrafish

Anesthesia-free Heartbeat Measurements in Freely Moving Zebrafish

Published on: April 18, 2025

RBF neural network center selection based on Fisher ratio class separability measure.

¹Sch. of Electr. and Electron. Eng., Nanyang Technol. Univ., Singapore.

IEEE Transactions on Neural Networks

|February 5, 2008

Summary

This study introduces a novel method for selecting radial basis function (RBF) neural network centers using the Fisher ratio to enhance classification. This approach optimizes feature vectors for maximum class separability and a parsimonious network architecture.

More Related Videos

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Related Experiment Videos

Last Updated: Jul 7, 2026

Anesthesia-free Heartbeat Measurements in Freely Moving Zebrafish

Anesthesia-free Heartbeat Measurements in Freely Moving Zebrafish

Published on: April 18, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

Machine Learning
Artificial Intelligence
Pattern Recognition

Background:

Radial basis function (RBF) neural networks map nonlinear data to a linear space via hidden layer neurons.
The discriminative power of RBF networks is determined by the selection of RBF centers.
Existing methods may not optimally select centers for maximum class separability.

Purpose of the Study:

To propose a novel method for selecting RBF centers based on the Fisher ratio class separability measure.
To achieve maximum discriminative power in classification tasks.
To develop a parsimonious RBF network architecture with enhanced class separation.

Main Methods:

Utilizing the Fisher ratio as a measure of class separability for RBF center selection.
Employing an orthogonal transform to decouple correlations among hidden layer neuron responses.
Implementing a multistep procedure combining Fisher ratio, orthogonal transform, and forward selection search.

Main Results:

The proposed method effectively selects RBF centers that maximize class separability.
The orthogonal transform allows for independent evaluation of class separability for individual RBF neurons.
The method results in a parsimonious network architecture.

Conclusions:

The Fisher ratio-based RBF center selection method enhances classification performance.
This approach leads to improved feature vectors and greater class separation.
The developed technique offers a robust strategy for designing effective RBF neural networks.