Differences between logistic regression and perceptrons

As I understand, a perceptron/single-layer artificial neural network with a logistic sigmoid activation function is the same model as logistic regression. Both models are given by the equation:

F(x) = \frac{1}{1-e^{-\beta X}}

The perceptron learning algorithm is online and error-driven, whereas the parameters for logistic regression could be learned using a variety of batch algorithms, including gradient descent and Limited-memory BFGS, or an online algorithm, like stochastic gradient descent. Are there any other differences between logistic regression and a sigmoid perceptron? Should the results of a logistic regressor trained with stochastic gradient descent be expected to be similar to the perceptron?


You mentioned already the important differences. So the results should not differ that much.

Source : Link , Question Author : gavinmh , Answer Author : Michael Dorner

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