L1-distance and classification problem by Bayesian method

DOI10.1080/02664763.2016.1174194OpenAlexW2346012900MaRDI QIDQ5138542

Tai Vo-Van

Publication date: 4 December 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2016.1174194

zbMATH Keywords

classificationdistancedensityBayes errormaximum function

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Pattern recognition, speech recognition (68T10)

Related Items (9)

Some results of classification problem by Bayesian method and application in credit operation ⋮ A new approach for face detection using the maximum function of probability density functions ⋮ Automatic genetic algorithm in clustering for discrete elements ⋮ An automatic fuzzy clustering algorithm for discrete elements ⋮ The fuzzy cluster analysis for interval value using genetic algorithm and its application in image recognition ⋮ Automatic clustering algorithm for interval data based on overlap distance ⋮ An automatic clustering for interval data using the genetic algorithm ⋮ A new binary adaptive elitist differential evolution based automatic \(k\)-medoids clustering for probability density functions ⋮ Classifying two populations by Bayesian method and applications

Uses Software

• Matlab

Cites Work

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• Bounds for the Bayes error in classification: a Bayesian approach using discriminant analysis
• Separation and probability of correct classification among two or more distributions
• Bayesian analysis in the \(L^{1}\)-norm of the mixing proportion using discriminant analysis
• On the notion of affinity of several distributions and some of its applications
• Error estimation in pattern recognition via<tex>L_alpha</tex>-distance between posterior density functions
• Statistical Discrimination Analysis Using the Maximum Function
• Some Inequalities Between Distance Measures for Feature Evaluation
• Sample-Based Classification Procedures Derived from Density Estimators

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