Particle Learning of Gaussian Process Models for Sequential Design and Optimization

Particle learning of Gaussian process models for sequential design and optimization

We develop a simulation-based method for the online updating of Gaussian process regression and classification models. Our method exploits sequential Monte Carlo to produce a thrifty sequential design algorithm, in terms of computational speed, compared to the established MCMC alternative. The latter is less ideal for sequential design since it must be restarted and iterated to convergence with...

Accelerated Learning of Gaussian Process Models

The Gaussian process model is an example of a flexible, probabilistic, nonparametric model with uncertainty predictions. It offers a range of advantages for modelling from data and has been therefore used also for dynamic systems identification. One of the noticeable drawbacks of the system identification with Gaussian process models is computation time necessary for modelling. The modelling pr...

Max-Margin Learning of Gaussian Mixtures with Sequential Minimal Optimization

This works deals with discriminant training of Gaussian Mixture Models through margin maximization. We go one step further previous work, we propose a new formulation of the learning problem that allows the use of efficient optimization algorithm popularized for Support Vector Machines, yielding improved convergence properties and recognition accuracy on handwritten digits recognition.

Study of some strategies for global optimization using Gaussian process models with application to aerodynamic design

Aerodynamic shape optimization using CFD and global optimizers like PSO is a computationally expensive process. To make the method more efficient, metamodels must be incorporated into the optimization algorithm. Metamodels can be either local or global in their approximation ability. We use local models based on kriging in a pre-evaluation strategy to screen a small number of promising designs,...

Gaussian process models for robust regression, classification, and reinforcement learning