Riedmiller and Braun's adaptive variant of this method uses the Manhattan method. Silva and Almeida's variant of this method includes the multipliers u and d, which should be greater and less 3 than 1, respectively. This method can be extended to recurrent and Elman networks by unfolding them "through time". A common way to speed up this method and avoid excessive oscillations is to include the "momentum term". This method rose to power following
Riedmiller and Braun's adaptive variant of this method uses the Manhattan method. Silva and Almeida's variant of this method includes the multipliers u and d, which should be greater and less 3 than 1, respectively. This method can be extended to recurrent and Elman networks by unfolding them "through time". A common way to speed up this method and avoid excessive oscillations is to include the "momentum term". This method rose to power following the 1986 publication of a paper 6 by (*) Rumelhart, Hinton, and Williams. It's often combined with a sigmoid activation function. This method is applied to networks with an input layer, one or more hidden layers, and an output layer, where each layer only feeds to the next, and it updates weights based on the gradient of the quadratic error function. For 109 points, name this very famous learning method that's applied to feedforward networks.
Riedmiller and Braun's adaptive variant of this method uses the Manhattan method.
backpropagation [prompt on: gradient descent method]