1 / 4

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

nile
Télécharger la présentation

Perceptron Models

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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)

More Related