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FEXPAC: a program for linear discriminant classification.

B Dahlqvist1

  • 1Image Analysis Laboratory, Uppsala, Sweden.

Computer Methods and Programs in Biomedicine
|March 1, 1988
PubMed
Summary
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A new program uses the Fisher linear discriminant function for feature extraction and classification. This tool aids in multivariate data analysis and designing linear classifiers from training data.

Area of Science:

  • Machine Learning
  • Data Science
  • Statistical Analysis

Background:

  • Multivariate data analysis presents challenges in feature extraction and classification.
  • Developing efficient linear classifiers is crucial for various applications.

Purpose of the Study:

  • To introduce a novel program for feature extraction and two-class classification.
  • To leverage the Fisher linear discriminant function model for enhanced analytical capabilities.

Main Methods:

  • Implementation of a new program for feature extraction.
  • Utilization of the Fisher linear discriminant function model for classification.
  • Development of a tool for designing and storing linear classifiers.

Main Results:

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  • The developed program effectively performs feature extraction.
  • The Fisher linear discriminant function model is successfully applied for two-class classification.
  • The program facilitates the creation and storage of linear classifiers.

Conclusions:

  • The new program offers a valuable tool for multivariate data analysis.
  • The Fisher linear discriminant function model proves effective for linear classification tasks.
  • This approach aids in the efficient design and storage of classifiers from training data.