Adaptive-Online-Learning

代码说明：

  基于EKF的神经网络自适应在线学习算法，包含例子和文档。(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.)

文件列表：

Adaptive Online Learning of Neural Networks with the EKF
........................................................\ekfdemo1.m,3911,1998-09-14
........................................................\mlpekf.m,3659,1998-09-09
........................................................\mlpekfQ.m,4652,1998-09-14
........................................................\neuralNetEKF.pdf,3369286,2012-03-20