Numerical taxonomy and the principle of maximum entropy
type: Item
instance of: scholarly article
title: Numerical taxonomy and the principle of maximum entropy
zbMATH Open document ID: 0868.92002
DOI: 10.1007/BF01246099
author: Mats Gyllenberg
author: Timo Koski
published in: Journal of Classification
publication date: 1996-12-08T00:00:00Z
Mathematics Subject Classification ID: 92B10
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62B10
zbMATH DE Number: 955276
zbMATH Keywords: Bhattacharyya coefficient
zbMATH Keywords: binary vectors
zbMATH Keywords: Hamming distance
zbMATH Keywords: Hellinger distance
zbMATH Keywords: hypothetical mean organism
zbMATH Keywords: mixture of multivariate Bernoulli distributions
zbMATH Keywords: monothetic classes
zbMATH Keywords: classification
zbMATH Keywords: maximum entropy
zbMATH Keywords: polythetic taxa
zbMATH Keywords: clustering
zbMATH Keywords: maximal predictivity
zbMATH Keywords: asymptotic separateness of maximum entropy distributions
zbMATH Keywords: identification errors
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 955276