Assertion: neural network's output | /r/HasProperty | continuous
Facets:
value: in adaline
type: LOCATION
value: between 0
type: OTHER
value: between 1
type: OTHER
Source sentences:
text: A single-layer neural network can compute a continuous output instead of a step function.
text: If the output is a continuous path, it is classified into regression.
text: The main difference between a sigmoid neuron and a perceptron is that the input and the output can be any continuous value between 0 and 1.
text: e^{-x}}{e^{x} - e^{-x}}. tanh(x)=ex−e−xex+e−x​. Use of any of these functions ensures the output is a continuous number (as opposed to binary), and thus not every activation function yields a linear classifier.
text: Again, the “output” is the continuous net input value in Adaline and the predicted class label in case of the perceptron; eta is the learning rate.
text: Both the input and output can be either continuous or discrete as well as they can be either deterministic or fuzzy.
text: The main difference between logistic and linear regression is in the type of output of the models; when our output is discrete, we talk about logistic regression, and when the output is continuous we talk about linear regression.