Variational Bayesian Approach to Support Vector Regression

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概要

• 論文の詳細を見る

• We consider a variational Bayesian approach to support vector regression (SVR). Its main advantage is that one can estimate the leave-one-out error of an SVR analytically without doing the cross-validation. Comparing our theory with the simulations on both artificial (the "sinc" function) and benchmark (the "Boston Housing") datasets, we get a good agreement. Furthermore, the smoothness of the hyperparameter dependence of the leave-one-out error can be tuned, which is useful for determining the optimal hyperparameters.

• 理論物理学刊行会の論文
• 2005-04-30

著者

• WONG K.

  Department of Applied Physics and Center for Smart Materials, Hong Kong Polytechnic University

• GAO Zhuo

  Department of Physics, Hong Kong University of Science and Technology

• Gao Zhuo

  Department Of Physics Hong Kong University Of Science And Technology:department Of Physics Beijing N

• Wong K.

  Department Of Applied Physics And Center For Smart Materials Hong Kong Polytechnic University

• GAO Zhuo

  Department of Physics, Hong Kong University of Science and Technology:Department of Physics, Beijing Normal University

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