Williams, C. K. I. & Rasmussen, C. E. (1996) Gaussian Processes for Regression Advances in Neural Information Processing Systems 8 MIT Press. Home/Search Document Not in Database Summary Related Articles Check |
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Gaussian Process Regression: Active Data Selection.. - Seo, Wallat.. (2000) (Correct)
....22 runs for ALM. from a realistic simulation of the dynamics of a Puma 560 robot arm. 1 We chose a covariance function of the form C(x m ; x n ) 2 1 exp h Gamma 1 2 P D i=1 w i (x m i Gamma x n i ) 2 i 4 P D i=1 x m i x n i 3 ffi (m; n) as suggested by [8] and determined suitable settings for the hyperparameters by the method of evidence maximization using cojugate gradient [8] After a randomly drawn seed data point, 250 data points were queried according to the ALM and ALC criteria for 400 randomly drawn data points, and using random selection. ....
.... the form C(x m ; x n ) 2 1 exp h Gamma 1 2 P D i=1 w i (x m i Gamma x n i ) 2 i 4 P D i=1 x m i x n i 3 ffi (m; n) as suggested by [8] and determined suitable settings for the hyperparameters by the method of evidence maximization using cojugate gradient [8]. After a randomly drawn seed data point, 250 data points were queried according to the ALM and ALC criteria for 400 randomly drawn data points, and using random selection. Due to the size of the data set the criteria were applied only to a subset of the whole data set. The estimation of the ....
Rasmussen, C.E. and Williams, C.K.I. Gaussian processes for regression Advances in Neural Information Processing Systems 8, MIT press,1996.
Gaussian Process Priors with Uncertain Inputs -.. - Girard, Rasmussen, al. (2003) (3 citations) Self-citation (Rasmussen) (Correct)
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Williams, C. K. I. & Rasmussen, C. E. (1996) Gaussian Processes for Regression Advances in Neural Information Processing Systems 8 MIT Press.
Gaussian Process Priors with Uncertain Inputs -.. - Girard.. (2003) (3 citations) Self-citation (Rasmussen) (Correct)
....attached to the forecast. An alternative solution is presented for iterative k step ahead prediction, with propagation of the prediction uncertainty. 2 Gaussian Process modelling We briefly recall some fundamentals of Gaussian processes. For a comprehensive introduction, please refer to [5] [11], or the more recent review [12] 2.1 The GP prior model Formally, the random function, or stochastic process, f(x) is a Gaussian process, with mean m(x) and covariance function C(x ) if its values at a finite number of points, are seen as the components of a normally distributed ....
Williams, C. K. I. & Rasmussen, C. E. (1996) Gaussian Processes for Regression Advances in Neural Information Processing Systems 8 MIT Press.
Multiple-step ahead prediction for non linear dynamic systems - .. - Girard, al. (2003) (1 citation) Self-citation (Rasmussen) (Correct)
....to the forecast. An alternative solution is presented for iterative k step ahead prediction, with propagation of the prediction uncertainty. 2 Predicting with Gaussian processes We briefly recall some fundamentals of Gaussian processes. For a comprehensive introduction, please refer to [3] [7], or the more recent review [8] 2.1 The GP prior model A formal definition of a Gaussian process is that of a random function f(x) with mean m(x) and covariance function C(x ) if its values f(x ) can be seen as the components of a normally distributed random vector. Here, we assume ....
Williams, C. K. I. & Rasmussen, C. E. (1996) Gaussian Processes for Regression Advances in Neural Information Processing Systems 8 MIT Press.
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