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In a single perceptron, the updation rule of weight vector is given by

1. $w(n+1) = w(n) + \eta [d(n)-y(n)]$
2. $w(n+1) = w(n) - \eta [d(n)-y(n)]$
3. $w(n+1) = w(n) + \eta [d(n)-y(n)]*x(n)$
4. $w(n+1) = w(n) - \eta [d(n)-y(n)]*x(n)$

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ans is C  f

Steps

1. Initialize the weights and the threshold. Weights may be initialized to 0 or to a small random value. In the example below, we use 0.
2. For each example j in our training set D, perform the following steps over the input and desired output :
1. Calculate the actual output:
2. Update the weights:
, for all features

for more details refer https://en.wikipedia.org/wiki/Perceptron
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