gaussian process regression model
Gaussian process regression model for normalization of LC-MS data using scan-level information
... the Gaussian process regression model (GPRM) that utilizes information from individual scans within an extracted ion chromatogram (EIC) of a ...
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Gaussian process regression model for dynamically calibrating and surveilling a wireless low-cost particulate matter sensor network in Delhi
... are prevalent but of similar magnitudes. Otherwise, quality predictions will only apply for those nodes whose means are close to the Delhi-wide mean. We showed that our al- gorithm performance is insensitive to training ...
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Modeling Daily Pan Evaporation in Humid Climates Using Gaussian Process Regression
... training process of GPR models because the central role in the Gaussian process regression model belongs to the covariance function K(x, ...
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Soft Sensor Development for the Measurement of CO2
... using Gaussian Process ...the Gaussian Process ...the Gaussian Process Regression model and the regression/prediction is found out of the amount of carbon ...
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A Novel Virtual Torque Sensor for Rescue Robots with Harmonic Drives
... precise model and position measurements avoids the need of traditional strain gauges and ...compliance model and the Gaussian process regression (GPR) technique are used together to ...
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Gaussian Kullback-Leibler Approximate Inference
... investigate Gaussian Kullback-Leibler (G-KL) variational approximate inference techniques for Bayesian generalised linear models and various ...of Gaussian covariance that make G-KL methods fast and ...
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GPLP: A Local and Parallel Computation Toolbox for Gaussian Process Regression
... GP regression with training data set x and the previously specified parameters, and ddmGP returns the trained model ( model ) and the elapsed time ( elpasedTrain ...
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Accelerating multiscale modelling of fluids with on the fly Gaussian process regression
... surrogate model for all micro-simulations, with the macro-model interpolating between data entries whenever it requires ...rogate model to replace costly micro-simulations, but can judge on-the-fly ...
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Dependent relevance determination for smooth and structured sparse regression
... multivariate Gaussian that controls the correla- tion structure governing conditionally independent densities over ...Bayesian model for structured sparsity that uses a Gaussian process (GP) ...
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Ensemble Kalman filtering for online Gaussian process regression and learning
... Another approach is the distributed computations, where local predictions are combined into unified mean and variance predictions. The idea of partitioning dataset for the GP prob- lem was considered in [8] with use of ...
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Statistical Methods for Computer Experiments with Applications in Neuroscience
... of Gaussian process regression and classification by particle learning ...hybrid regression-classification GP model, sequential design for exploring classification boundaries by the ...
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Estimating Multiparameter Partial Expected Value of Perfect Information from a Probabilistic Sensitivity Analysis Sample: A Nonparametric Regression Approach
... study model is computationally cheap to evaluate, we assume that we are in a position of being able to evaluate the model only 10 000 ...000 model runs in total (see Table 2 for values of J and ...
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A Search For New Low-Mass Diphoton Resonances At Atlas And An Investigation Into Using Gaussian Process Regression To Model Non-Resonant Two-Photon Standard Model Backgrounds
... Chapter 4. After qualifying for authorship, I began the work that would make up the bulk of this thesis. Experimental signatures with two photons in the final state provide clean processes with relatively small ...
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Random Walk Kernels and Learning Curves for Gaussian Process Regression on Random Graphs
... We can extend the scope of this qualitative comparison by examining how a student GP with a kernel with one normalisation performs when learning from a teacher with a kernel with the other normalisation. This is a case ...
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Spatio-Temporal Structured Sparse Regression With Hierarchical Gaussian Process Priors
... We show that the introduced model can be applied to different areas such as compressive sensing and EEG source localisation. The results show the superiority of the proposed method in comparison with the ...
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Efficient Computation of Gaussian Process Regression for Large Spatial Data Sets by Patching Local Gaussian Processes
... ducing inputs (M ∈ {100, 150, 200, 250, 300, 400, 500, 600, 700}) for all of the data sets. We used the k-means clustering for splitting training data for both of BCM, RBCM, and PIC. Figure 9 shows the logarithms of MSEs ...
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Determination of Strain Energy for Triggering Liquefaction Based on Gaussian Process Regression
... required for triggering liquefaction (W). The developed GPR model has been compared with the ANN model. The developed GPR has been used to determine the variance of predicted W. The rest of this paper is ...
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A spatio temporal, Gaussian Process Regression, real estate price predictor
... time cube to provide real-estate sales temporal singularity; (ii) Produced a spatially aware UnK calculation identifying house price spatial dependencies; (iii) Introduced a number of property, economic and network ...
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Tracking by fusion via Gaussian Process Regression extended to transfer learning
... box regression in [24], the cross correlation in [25] and the saliency- map-based generative model in [23] preceded by a CNN are all dedicated to preventing the inconsistency between classification and ...
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Adaptive CSI and feedback estimation in LTE and beyond: a Gaussian process regression approach
... the model has converged to the optimal decision compared to the state of the art where no prediction is used and the CSI is sam- pled every 2 ...dual-control model has as input the packet loss of each user, ...
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