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Learning rule. An artificial neural network 's learning rule or learning process is a method, mathematical logic or algorithm which improves the network's performance and/or training time. Usually, this rule is applied repeatedly over the network. It is done by updating the weights and bias levels of a network when a network is simulated in a ...
The learning rule used by ADALINE is the LMS ("least mean squares") algorithm, a special case of gradient descent. Define the following notations: is the learning rate (some positive constant) is the output of the model; is the target (desired) output
D2L (or Desire2Learn) is a Canada-based global software company with offices in Australia, Brazil, Europe, Singapore, and the United States.. D2L is the developer of the Brightspace learning management system, a cloud-based software suite used by schools, higher educational institutions, and businesses for online and blended classroom learning.
Stochastic gradient descent is a popular algorithm for training a wide range of models in machine learning, including (linear) support vector machines, logistic regression (see, e.g., Vowpal Wabbit) and graphical models. [21] When combined with the back propagation algorithm, it is the de facto standard algorithm for training artificial neural ...
This cost function (()) is the mean square error, and it is minimized by the LMS. This is where the LMS gets its name. Applying steepest descent means to take the partial derivatives with respect to the individual entries of the filter coefficient (weight) vector
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