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172 449 (0,428s)

Result

Use of Gaussian Process Regression in Processing Scientific Data

We present the method of Gaussian Process Regression (GPR) and discuss the issues of training and prediction and their efficient realization. Further, we consider the application of GPR in design optimization and modeling e...

JU - Aeronautika, aerodynamika, letadla

  • 2009
  • D
Result

Comparison of ordinal and metric gaussian process regression as surrogate models for CMA evolution strategy

transformations suggests ordinal regression, whereas Gaussian process continuity suggests metric regression. Results of testing ordinal and metric Gaussian process regressionIn this pape...

Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

  • 2017
  • D
  • Link
Result

OctGPR package for Octave-Forge

The OctGPR package is part of the open source project OctaveForge. It is an installable package for the Octave environment, providing functions for nonparametric regression and error prediction using the method of Gaussian Proce...

JC - Počítačový hardware a software

  • 2008
  • X
Result

Data-driven kinetic energy density fitting for orbital-free DFT: Linear vs Gaussian process regression

and Gaussian process regressions of KEDs for light metals and a semiconductor. We find-volume dependence with linear regressions but substantially improves it with Gaussian process regression...

Physical chemistry

  • 2020
  • Jimp
  • Link
Result

Functional Dual Adaptive Control with Recursive Gaussian Process Model

uncertainties in the system description are modelled by a non-parametric Gaussian process regression model. Current approaches to adaptive control based on Gaussian process models propose the use of recur...

Automation and control systems

  • 2015
  • D
  • Link
Result

Testing Gaussian Process Surrogates on CEC’2013 Multi-Modal Benchmark

on Gaussian processes, but on ordinary regression by means of support vector machines hasThis paper compares several Gaussian-processbased surrogate modeling methods applied to black-box optimization by means of t...

IN - Informatika

  • 2016
  • D
  • Link
Result

Dependencies in Stochastic Geometry - A Simulation Study

The background of sliced inverse regression with applications in stochastic models dependent on a multidimensional Gaussian random field is presented process withintensity being a function of the random field. Then the poin...

BA - Obecná matematika

  • 2011
  • D
Result

Gaussian process regression ́s hyperparameters optimization to predict financial distress

Predicting financial distress has become one of the most important topics of the hour that has swept the accounting and financial field due to its significant correlation with the development of science and technology. The main objective of this pape...

Economics and Business

  • 2023
  • Jimp
  • Link
Result

Investigation of Gaussian Processes and Random Forests as Surrogate Models for Evolutionary Black-Box Optimization

This paper introduces two surrogate models for continous black-box optimization, Gaussian processes and random forests, as an alternative to the already used ordinal SVM regression. We employ the CMA-ES as the reference opt...

IN - Informatika

  • 2015
  • D
  • Link
Result

Bayesian transfer learning between Gaussian process regression tasks

and target tasks are to perform Gaussian process regression. Experimental evaluation...

Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

  • 2019
  • D
  • Link
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