Classification of binary vectors by stochastic complexity.

DOI10.1006/jmva.1997.1687zbMath1090.62542OpenAlexW1968801151MaRDI QIDQ1372217

Martin Verlaan, Mats Gyllenberg, Timo Koski

Publication date: 1997

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1006/jmva.1997.1687

zbMATH Keywords

classificationprinciple of maximum entropymaximum likelihood estimateinformation contentAUTOCLASSbacterial identificationmaximal predictive classificationmixture of multivariate Bernouli distributionsuniversal source codes

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Statistical aspects of information-theoretic topics (62B10)

Related Items

Probabilistic Models for Bacterial Taxonomy, On multivariate binary data clustering and feature weighting, Learning genetic population structures using minimization of stochastic complexity, A unified view on clustering binary data, Bayesian unsupervised classification framework based on stochastic partitions of data and a parallel search strategy, Have I seen you before? Principles of Bayesian predictive classification revisited, Bayesian predictiveness, exchangeability and sufficientness in bacterial taxonomy

Uses Software

• AutoClass

Cites Work

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