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Perceptron Models

Perceptron Models. The perceptron is a kind of binary classifier that maps its input x (a vector of type Real ) to an output value f ( x ) (a scalar of type Real) calculated as f(x) = <w,x> + b

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Perceptron Models

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  1. Perceptron Models The perceptron is a kind of binary classifier that maps its input x (a vector of type Real) to an output value f(x) (a scalar of type Real) calculated as f(x) = <w,x> + b where w is a vector of real-valued weights and is the dot product(which computes a weighted sum). b is the 'bias', a constant term that does not depend on any input value.(x)

  2. x(j) denotes the j-th item in the input vector • w(j) denotes the j-th item in the weight vector • y denotes the output from the neuron • δ denotes the expected output • α is a constant and 0 < α < 1 • the appropriate weights are applied to the inputs that passed to a function which produces the output y • The weights are updated after each input according to the update rule below: • w(j)' = w(j) + α(δ − y)x(j)

  3. Famous Minsky and Papert Book:Perceptrons (1969) Showed that Perceptrons couldn’t solve general simple nonseperable problems (eg. XOR)

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