کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1180518 1491536 2015 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Gaussian process regression with multiple response variables
ترجمه فارسی عنوان
رگرسیون فرآیند گاوسی با متغیرهای پاسخ چندگانه
موضوعات مرتبط
مهندسی و علوم پایه شیمی شیمی آنالیزی یا شیمی تجزیه
چکیده انگلیسی


• We propose a direct formulation of the covariance function for multi-response Gaussian process regression.
• The proposed model is able to learn from the data dependencies between different outputs.
• The superiority of the proposed multi-response GPR method over the independent GPR is demonstrated through numerical examples.
• The multi-response GPR model is particularly effective for cases where different responses may be observed at different covariate values.

Gaussian process regression (GPR) is a Bayesian non-parametric technology that has gained extensive application in data-based modelling of various systems, including those of interest to chemometrics. However, most GPR implementations model only a single response variable, due to the difficulty in the formulation of covariance function for correlated multiple response variables, which describes not only the correlation between data points, but also the correlation between responses. In the paper we propose a direct formulation of the covariance function for multi-response GPR, based on the idea that its covariance function is assumed to be the “nominal” uni-output covariance multiplied by the covariances between different outputs. The effectiveness of the proposed multi-response GPR method is illustrated through numerical examples and response surface modelling of a catalytic reaction process.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 142, 15 March 2015, Pages 159–165
نویسندگان
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