Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Improved Fast Gauss Transform and Efficient Kernel Density Estimation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
The kernel recursive least-squares algorithm
IEEE Transactions on Signal Processing
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Mixtures of predictive linear Gaussian models for nonlinear stochastic dynamical systems
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Fast learning algorithm for Gaussian models to analyze video objects with parameter size
ETFA'09 Proceedings of the 14th IEEE international conference on Emerging technologies & factory automation
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The recent Predictive Linear Gaussian model (or PLG) improves upon traditional linear dynamical system models by using a predictive representation of state, which makes consistent parameter estimation possible without any loss of modeling power and while using fewer parameters. In this paper we extend the PLG to model stochastic, nonlinear dynamical systems by using kernel methods. With a Gaussian kernel, the model admits closed form solutions to the state update equations due to conjugacy between the dynamics and the state representation. We also explore an efficient sigma-point approximation to the state updates, and show how all of the model parameters can be learned directly from data (and can be learned on-line with the Kernel Recursive Least-Squares algorithm). We empirically compare the model and its approximation to the original PLG and discuss their relative advantages.