Title:
Adaptive-Online-Learning
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Description: We show that a hierarchical Bayesian modeling approach allows us to perform
regularization in sequential learning. We identify three inference
levels within this hierarchy: model selection, parameter estimation, and
noise estimation. In environments where data arrive sequentially, techniques
such as cross validation to achieve regularization or model selection
are not possible. The Bayesian approach, with extended Kalman filtering
at the parameter estimation level, allows for regularization within
a minimum variance framework. A multilayer perceptron is used to generate
the extended Kalman filter nonlinear measurements mapping. We
describe several algorithms at the noise estimation level that allow us to
implement on-line regularization.We also show the theoretical links between
adaptive noise estimation in extended Kalman filtering, multiple
adaptive learning rates, and multiple smoothing regularization coefficients.
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Adaptive Online Learning of Neural Networks with the EKF\ekfdemo1.m
........................................................\mlpekf.m
........................................................\mlpekfQ.m
........................................................\neuralNetEKF.pdf
Adaptive Online Learning of Neural Networks with the EKF
