Questions tagged [perceptron]
An early example of neural network without any hidden layers and with a single (possibly nonlinear) output unit.
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What's the difference between logistic regression and perceptron?
I'm going through Andrew Ng's lecture notes on Machine Learning.
The notes introduce us to logistic regression and then to perceptron. While describing Perceptron, the notes say that we just change ...
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Can a perceptron with sigmoid activation function perform nonlinear classification?
Consider the perceptron as illustrated in the figure above.
I know:
If the activation function is linear, i.e. the first three cases, then
the perceptron is equivalent to a linear classifier.
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From the Perceptron rule to Gradient Descent: How are Perceptrons with a sigmoid activation function different from Logistic Regression?
Essentially, my question is that in multilayer Perceptrons, perceptrons are used with a sigmoid activation function. So that in the update rule $\hat{y}$ is calculated as
$$\hat{y} = \frac{1}{1+\exp(...
user39663
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Clarification about Perceptron Rule vs. Gradient Descent vs. Stochastic Gradient Descent implementation
I experimented a little bit with different Perceptron implementations and want to make sure if I understand the "iterations" correctly.
Rosenblatt's original perceptron rule
As far as I understand, ...
user39663
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Can non-linearly separable data always be made linearly separable?
A data set that is linearly separable is a precondition for algorithms like the perceptron to converge. It's well-known that we can project low-dimensional data to a higher dimension using kernel ...
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Perceptron overfitting?
I'm trying to judge the performance of my perceptron linear discriminant. In one instance I'm training on a sample size of 150 and on another I'm training on a sample size of 1500. I test both of ...
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Would multilayer perceptrons be better than multiple regression?
I am using multiple regression to predict the future value of a time series from several other time series. Would doing this with multilayer perceptrons produce better results than multiple regression?...
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Bias input in neural network
Does bias input work like constant value in linear regression? and if bias input is not used then resulting boundary will always pass through origin?
Thanks in advance.
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Neural networks: how can convex optimization produce different weights each time?
I am training a multilayer perceptron with a logistic activation function by backpropagation. The weights are not unique - each time I redo the fit, I get a different set of weights. However the ...
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