SVM-like decision theoretical classification of high-dimensional vectors

DOI10.1016/J.JSPI.2009.09.001zbMath1177.62083OpenAlexW2011956389MaRDI QIDQ1044064

David J. Bradshaw, Marianna Pensky

Publication date: 10 December 2009

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2009.09.001

zbMATH Keywords

simulationssupport vector machineposterior probabilitiesdecision theoretical approach

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Bayesian problems; characterization of Bayes procedures (62C10) Learning and adaptive systems in artificial intelligence (68T05) Statistical decision theory (62C99)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Decision theory classification of high-dimensional vectors based on small samples
• Classification, clustering, and data analysis. Recent advances and applications. Papers presented at the eighth conference of the International Federation of Classification Societies (IFCS), Cracow, Poland, July 16--19, 2002
• Bayesian Classification of Tumours by Using Gene Expression Data
• Soft and hard classification by reproducing kernel Hilbert space methods
• Multicategory Support Vector Machines

This page was built for publication: SVM-like decision theoretical classification of high-dimensional vectors