Robust kernel distance multivariate control chart using support vector principles

DOI10.1080/00207540500543265zbMath1153.91755OpenAlexW2060497214MaRDI QIDQ3532782

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Publication date: 28 October 2008

Published in: International Journal of Production Research (Search for Journal in Brave)

Full work available at URL: http://dspace.lib.cranfield.ac.uk/handle/1826/6882

zbMATH Keywords

support vector machinescontrol chartkernel distance

Mathematics Subject Classification ID

Production theory, theory of the firm (91B38) Statistical methods; economic indices and measures (91B82)

Related Items (6)

Predictive modeling in a steelmaking process using optimized relevance vector regression and support vector regression ⋮ A Shewhart-type nonparametric multivariate depth-based control chart for monitoring location ⋮ General support vector representation machine for one-class classification of non-stationary classes ⋮ Relevance vector machine with tuning based on self-adaptive differential evolution approach for predictive modelling of a chemical process ⋮ Nonparametric control charts based on data depth for location parameter ⋮ Shifting artificial data to detect system failures

Cites Work

• Unnamed Item
• Support vector data description
• A Class of Distribution-Free Control Charts
• A Multivariate Exponentially Weighted Moving Average Control Chart
• A Markov Chain Model for the Multivariate Exponentially Weighted Moving Averages Control Chart
• X-bar andRcontrol chart interpretation using neural computing
• A kernel-distance-based multivariate control chart using support vector methods
• A Quality Index Based on Data Depth and Multivariate Rank Tests
• Support vector machines for recognizing shifts in correlated and other manufacturing processes

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