As a proof-of-concept, we evaluate our approach on complex non-smooth functions where standard GPs perform poorly, such as step functions and robotics tasks with contacts.
Good sparse approximations are essential for practical inference in Gaussian Processes as the computational cost of exact methods is prohibitive for large datasets. Our numerical evaluations demonstrate the robustness of the proposed approach in situations where other state-of-the-art Gaussian filters and smoothers can fail.
This feature space is often learned in an unsupervised way, which might lead to data representations that are not useful for the overall regression task.
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Antithetic and Monte Carlo kernel estimators for partial rankings.
An efficient road transportation system is of vitally important for smooth operations of industrial units. Impact of land- use changes on the processes in the Elbow river watershed in southern Alberta. We introduce a conceptually novel structured prediction model, GPstruct, which is kernelized, non-parametric and Bayesian, by design.
Fragmentation threshold is one of the parameters used in a wireless local area network which specifies the values to decide if the Media Access Control MAC Service Data Unit MSDU received from the higher layer network needs fragmentation before transmission.
The proposed GPCM is validated using synthetic and real-world signals. As a result of this simplification, the computational complexity of the detection is lowered significantly. GPs are gaining increasing importance in signal processing, machine learning, robotics, and control for representing unknown system functions by posterior probability distributions.
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Pavement, maintenance, management rehabilitation, transportation Reference 1. As PILCO is built on the probabilistic Gaussian processes framework, additional system knowledge can be incorporated by defining appropriate prior distributions, e.
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This thesis will explore the possibilities of convex optimization for metric learning.
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Ajax Toolkits such as Dojo allow web developers to build Web 2. Consistent kernel mean estimation for functions of random variables.
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