Questions tagged [perceptron]
For questions about the perceptron learning algorithm in Machine Learning.
56 questions
2 votes
1 answer
46 views
Is GLM(general linear mode) same as reparameterization?
So during a project I get to know the reparameterization trick, where you restructure the mathematical expression hence to train parameters which is un-trainable prior to the restructuring. Now I am ...
- 123
1 vote
1 answer
95 views
What do they mean by "randomly generate data in 2d plane for 2 classes"?
so we got this for our lab, I just need help understanding number 2. When it say generate data in 2D plane is it telling us to only generate input with just 2 characteristics(X and Y co-ord)? Or can ...
- 31
1 vote
1 answer
76 views
What is the difference between SLP and perceptron and do they require non-linearity or not?
I couldn't find the exact answer to my question because the discussions tend go somewhere else, so I want to ask: What is a single-layer perceptron and what makes it different from 'perceptron'? Are ...
- 81
1 vote
1 answer
110 views
What is the definition of a perceptron?
What is the definition of a perceptron? OK, you are right, this question looks like lacking effort; it doesn't. On the net, I have found many definitions that restrict the input of a perceptron to $n$ ...
- 113
1 vote
2 answers
141 views
Direct formula for calculating the optimum matrix which minimizes the perceptron error
Suppose we have a perceptron without bias and $f(x) = x$ as activation function and matrices $X,Y,W$ that input training data are columns of matrix $X$, $Y$ is targets matrix (columns are ordered with ...
- 113
3 votes
2 answers
5k views
0 votes
1 answer
186 views
Is the Adaline just a Perceptron that uses Linear activation function and MSE cost function, and all the rest of steps are the same of Perceptron?
https://en.wikipedia.org/wiki/ADALINE https://pt.wikipedia.org/wiki/Perceptron I have a doubt about this: is the Adaline just a Perceptron that uses Linear activation function and MSE cost function, ...
- 267
1 vote
1 answer
275 views
Why does sklearn perceptron converge for linearly inseparable data points?
I learned that the perceptron algorithm only converges if the dataset is linearly separable. I am implementing this algorithm using scikit learn. The blue and orange points are from the training set, ...
0 votes
1 answer
146 views
How does a sigmoid neuron act like a perceptron in this scenario?
I have been reading Michael Nielsen’s book online on his website at http://neuralnetworksanddeeplearning.com/chap1.html. I am struggling to understand the second exercise: When c approaches infinity, ...
1 vote
0 answers
128 views
What is the most accurate way of building a Perceptron using only NumPy?
For context, I am trying to write a bunch of neural network programs using no other packages besides NumPy for educational purposes. I am trying to make them as simple as possible, i.e. removing the ...
user55176
3 votes
2 answers
338 views
Is there any variant of perceptron convergence algorithm that ensures uniqueness?
The perceptron convergence algorithm given below ensures the convergence of weights of the perceptron provided enough data points and iterations. Although it ensures convergence by finally getting a ...
- 4,172
2 votes
0 answers
53 views
Was the original perceptron machine really just comparing images to the "average image for each class"?
I've been looking into the history of artificial neural networks, and only recently learned that the original Mark 1 Perceptron was only a single layer network. It would iteratively modify the ...
- 21
1 vote
1 answer
472 views
Why are today's neural networks not modeled with probability theory?
In the paper The Perceptron: A probabilistic model for information storage and organization in the brain, Rosenblatt used the probability theory to model his perceptron. My professor told me that ...
- 11
2 votes
0 answers
732 views
Did the unsolved XOR problem in "Perceptrons: An Introduction to Computational Geometry" 1969 book really cause the winter of the AI in 1974?
Winter of AI definition: periods of reduced funding and interest in artificial intelligence research, due to unmet expectations after a period of hype. There have been at least two major AI winters ...
- 278
0 votes
1 answer
84 views
How to show $\rho > 0$ when $\rho$ be minimum attainable from $y_n(W^{*T}X_n)$, where $W^*$ the vector that separates the data?
In the book Learning from Data written (by Abu Mostafa), we have the following exercise: Let $\rho$ be minimum attainable from $y_n(W^{*T}X_n)$ where $W^*$ is the vector that separates the data. Show ...
- 101