6 Questions
Adaline consists of multiple output units with bipolar values (+1, -1).
False
The learning rule in Adaline aims to maximize the mean square error between activation and target values.
False
In Adaline, the weights between the input unit and output unit are fixed and not adjustable.
False
Adaline network applies the activation function first and then calculates the net input.
False
Adaline has a bias of activation function 0 instead of 1.
False
The delta learning rule is used in Adaline to update weights based on the comparison of actual output with calculated output.
True
Learn about Adaline, a network with a single linear unit and a linear activation function. Understand the adjustable weights, learning rule, and mean square error minimization in Adaline.
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