Questions tagged [perceptron]
Perceptron is a basic linear classifier that outputs binary labels.
80 questions
2 votes
1 answer
57 views
Why does the perceptron decision boundary "pass through the origin" after applying the bias trick?
I'm watching Lecture 4 ("Curse of Dimensionality / Perceptron") from Cornell’s CS4780 (Spring 2017) by Prof. Kilian Weinberger on YouTube. In this lecture, he applies the bias trick to ...
- 23
1 vote
1 answer
118 views
Is it possible to train a single input->neuron->relu->neuron->relu for input > 0.5?
The neural network is simply: y=max(max(x*w+b,0)*v+d,0) w,b is weight and bias of first neuron. v,d is weight and bias of second neuron. If data is for example: <...
- 11
0 votes
0 answers
144 views
Classical Perceptron Algorithm taking too long to evaluate on un-normalized data
I need to implement classical perceptron algorithm from scratch using numpy and pandas for an assignments. I have done so using this algorithm: I have a linearly seperable dataset of 568 rows and 30 ...
- 1
0 votes
1 answer
555 views
Can a multilayer perceptron classify binary values?
I have a dataset in which the response variable is Sick(1) or not sick (2). As for the variables, there are a few numeric ones (2/14), all the others are variables by levels (example: 1-Abdominal pain,...
- 334
0 votes
1 answer
74 views
Why we use an activation function for introducing nonlinearity instead of a polynomial Perceptron implementation?
I perceive a single perceptron as a single linear function $y = a_1x_1 + a_2x_2 + ... + a_nx_n + b_0$ with a goal to calculate the best weights combination $ w_1, w_2, ..., w_n $ that minimizes the ...
- 3
1 vote
1 answer
188 views
Visualizing a Perceptron
I wanted to visualize how a perceptron learns, so I made a class that performs gradient descent. To show the decision, I plot a plane showing where positive examples and negative examples are, ...
- 113
1 vote
1 answer
54 views
What issue is there, when training this network with gradient descent? [closed]
Suppose we have the following fully connected network made of perceptrons with a sign function as the activation unit, what issue arises, when trying to train this network with gradient descent?
- 131
1 vote
0 answers
41 views
why use one regularisation technique over another?
why should I prefer L1 over L2, in fully-connected-layer or convolution? why use dropout between 2 layers, when there is the option of regularising a layer(or both) with something like L1 or L2? and ...
2 votes
1 answer
1k views
How do I include the Bias term in the Pegasos algorithm?
I have been asked to implement the Pegasos algorithm as below. It is similar to the Peceptron algorithm but includes eta and lambda terms. However, there is no bias term below and I don't know how ...
1 vote
3 answers
111 views
Why there is only one type of artificial neuron?
I find it strange that so many deep learning tricks and improvements have been invented in the past decade but I never heard about someone trying out different models of the artificial neuron other ...
- 205
1 vote
0 answers
36 views
predict multiple letters in pixels matrix
I have a multilayer perceptron model that is trained to recognize handwritten English letters from an image. In the training set each image matrix had 784 pixel values. The labels of these images ...
- 111
1 vote
2 answers
54 views
Should a bias neuron be connected to previous neurons?
I'm trying to add a bias neuron to my neural network that uses the backpropagation algorithm. I'm trying to figure out how I should go about this, should I treat the bias neuron as a regular neuron? ...
- 131
1 vote
0 answers
39 views
I'm worried that I'm training my model wrong
So I'm trying to classify some fashion mnist like photos into either boots or sneakers. I'm using a perception from sklearn to do so. The data set is a CSV containing pixel values. The model is ...
- 35
2 votes
1 answer
506 views
Threshold logic unit (McCulloch-Pitts 1943) vs. Perceptron (Rosenblatt 1958)
I have searched various sources to find out what distinguishes the McCulloch-Pitts neuron from the perceptron invented by Rosenblatt. In most sources only one of these elements is considered, in ...
- 21
1 vote
1 answer
473 views
Calculation of VC dimension of simple neural network
Suppose I have a perceptron with one-hidden layer, with the input - one real number $x \in \mathbb{R}$, and the activation function of the output layers - threshold functions: $$ \theta(x) = \begin{...
