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