Model Learning with Local Gaussian Process Regression
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Summary
A local approximation to the standard GPR, called local GPR (LGP), is proposed for real-time model online learning by combining the strengths of both regression methods, i.e., the high accuracy of GPR and the fast speed of LWPR.
- Type
- article
- Published
- 2009-01-01
- Cited by
- 346
- References
- 34
- OpenAlex
- https://openalex.org/W2007864935
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:469531
Keywords
Inverse dynamics, Ground-penetrating radar, Kriging, Computer science, Gaussian process
References
- Approximation of Gaussian process regression models after training
- Robot dynamics and control
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- Local and global sparse Gaussian process approximations
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- Computed torque control with nonparametric regression models
- Incremental Online Learning in High Dimensions
- Introduction to ROBOTICS mechanics and control
- Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
- Introduction to Robotics Mechanics and Control
- Composite adaptive control with locally weighted statistical learning
- Bayesian Gaussian process models : PAC-Bayesian generalisation error bounds and sparse approximations
- Local Gaussian process regression for real-time model-based robot control
- Sparse incremental learning for interactive robot control policy estimation
- Fast Gaussian Process Regression using KD-Trees
- Experiments in nonlinear adaptive control
- Sparse On-Line Gaussian Processes
- Local Gaussian Process Regression for Real Time Online Model Learning
- Real-time robot learning with locally weighted statistical learning
- Bayesian Kernel Shaping for Learning Control
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- Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regression
- Electricity Demand Forecasting using Gaussian Processes
- A stochastic method for representation, modelling and fusion of excavated material in mining
- Rapid and reactive robot control framework for catching objects in flight
- Many regression algorithms, one unified model: A review
- UAV Parameter Estimation with Gaussian Process Approximations
- Model learning for robot control: a survey
- Large scale material science data analysis
- Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning
- A mixed-kernel-based SVR controller for biped robots
- Dynamic Structure Embedded Online Multiple-Output Regression for Stream Data
- Data-driven differential dynamic programming using Gaussian processes
- Conservative decision-making and interference in uncertain dynamical systems
- Variational inference for sparse spectrum Gaussian process regression
- A SVM controller for the stable walking of biped robots based on small sample sizes
- Autonomous online generation of a motor representation of the workspace for intelligent whole-body reaching
- Humanoid robot posture-control learning in real-time based on human sensorimotor learning ability
- Combined regression and classification approach for prediction of driver's braking intention
- A review on robot learning and controlling: imitation learning and human-computer interaction
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