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 is the Perceptron Kernel predictor function?

I am trying to implement a kernalised perceptron, and one thing that I can't understand is what at the end is the predictor function and how do we use it? I know that the update rule is $$\left (y_i \...
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How to draw the single perceptron decision boundary when weights and bias are 0?

I've been following an algorithm described on a book called Knowledge Discovery with Support Vector Machines by Lutz H. Hamel. In the book, there is this learning algorithm for a single perceptron ...
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Is the data linearly separable? [duplicate]

I have built a multiclass perceptron, which predicts the values of the Iris dataset. To more accurately classify the data points of the Iris Dataset, I used a Kernel to define a non-linear manifold ...
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Perceptron - Never find the AND solution

I'm trying to understand the roots of neural networks, and that's why I'm trying to code the perceptron. This is my class: ...
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About Multi-layer Perceptrons

I've always been a bit confused when it comes to Deep Learning terminology. Is the definition of the perceptron, whether single layer or multi layer, associated with a specific type of activation ...
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Rule of thumb for Data Requirements when Designing a Neural Network for Deep Learning

I'm designing an MLP classifier and I've been noticing that: Using a very shallow network, or one whose at least one layer has a small number of neurons yields bad performance Using a deep network ...
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Perceptron as a Logistic Regression

If by following way single perceptron is made to work like Logistic Regression. How much correct is it to say that I made perceptron to work as Logistic Regression. Question came to mind as ...
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Why do we use Matrix in Perceptron instead of Functions?

Matrices are good objects to store connections between dimensions/entities. However, matrix computation is often time consuming and sometimes wasteful if matrix is too sparse. Also thinking about the ...
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Why in gated recurrent unit gates are controlled by only one layer perceptrons?

Why don't I see a GRU anywhere with more than one layer of perceptrons inside, it's pretty obvious to try to put more layers in there, but I don't see anyone doing that
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Whats the different between Logistic regression and perceptron?

In a binary classification problem, if both logistic regression and a single preceptron uses Sigmoid function, what's the difference in classification results, since they will have the same decision ...
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Calculate the confidence score (decision_function) of perceptron, by the signed distance of that sample to the hyperplane

I've implemented the binary version of perceptron from scratch, in python. I would like to use it for one vs all classification, by using the one vs all of sklearn. for that, I need to implement the ...
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Can we express CNNs in terms of a MLP?

I have been wondering whether a convolution can be represented in terms of an MLP. We can say that in convolution we have shared parameters between different neurons. But how to express this ...
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Why aren't neural networks used with RBF activation functions (or other non-monotonic ones)?

In most work I've seen, MLPs (multilayer perceptron, the most typical feedforward neural network) and RBF (radial basis function) networks are compared as distinct models, where MLP neuron outputs $\...
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Neural network regression with constraint

I am training a neural network for a regression task, where the dependent variable varies in the range from $0$ to $10$. Unsurprizingly, with the test data set, I obtain the predictions that fall ...
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Can a 4D perceptron be plotted in 2 dimensions?

I am wondering if it is at all possible to plot a 4D perceptron line in 2D. Obviously, it would be impossible to observe it with all of its original information, but is there a way for me to observe ...
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Can we use perceptron training algorithm to train a single neuron with sigmoid activation?

The perceptron training algorithm is summarized as: Apply the inputs and calculate the output $ y $ Compare with the desired output yd and calculate error $e = y-y_d$ Update the weights based on the ...
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SciKit Learn: Multilayer perceptron early stopping, restore best weights

In the SciKit documentation of the MLP classifier, there is the early_stopping flag which allows to stop the learning if there is not any improvement in several ...
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Weight update rule for Rosenblatt's Perceptron

I'm wondering if anybody can explain how Rosenblatt reached his formula for updating the weights of his Perceptron: $w_{i, j} = w_{i, j} +\eta ( y_j - \hat{y}_j ) x_j$ It seems to me that the step ...
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Use a multilevel logistic regression and cross validation

I want to use a multilevel logistic regression for a double purpose, estimating the value of coefficients to explain a phenomenon. At the same time, I want to split the data through cross-validation ...
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Am I fundamentally misunderstanding the net input dot product w*x

Most books have the notation of a weight vector w and input matrix x: $$ w = \begin{bmatrix} w_1\\...\\ w_D \end{bmatrix}, x = \begin{bmatrix} x_{11}&...&x_{1D}\\ ...&...&...\\ x_{N1}&...
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Equivalence of Deep Feed-Forward Neural Network to Single-Layer Network

I am new to Deep Learning, and I was going through some lecture notes I found online. It said that a feed-forward neural network with $n$ hidden layers and only linear activation functions is ...
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Can we get the input from a multilayer perceptron based on the output of one of its hidden layers?

I was reading a relatively new paper that proposed to split a nerual networks layers into groups and sending each group to different nodes to train them in a distributed manner. In order to not send ...
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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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Kernelized perceptron algorithm weights update

I'm asked to find the maximum margin decision surface separating positive from negative samples by inspection. The positive examples are (1,1) and (-1,-1), the negative ones are (1,-1) and (-1,1). The ...
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321 views

Can a Single Layer Perceptron Learn a Nonlinear Function?

A lot of times I've read that with a Single Layer Perceptron (SLP), you can only learn linear functions. But what if we use a generalized linear model? Imagine we only have one input feature $x_1$. ...
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Why the weight vector is a linear combination of the inputs and the outputs in the Perceptron

I was studying Support Vector Machines and I've got stuck with this relation regarding the weight vector of the hyperplane. $w=\sum\limits_{i\in I}^{} y_i x_i$ For reference, I'm studying from the ...
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LabelEncoder with a Multi-Layer Perceptron?

So we're working on a machine learning project at work and it's the first time I'm working with an actual team on this. I got pretty good results with a model that uses the following SKLearn pipeline: ...
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SSE over epochs for MLPClassifier

I have a simple csv dataset I want to do something simple. Use the multilayer perceptron algorithm and plot SSE over epochs. I am a novice, I have searched a lot but cannot find a good solution. How ...
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Why is the equation for a single-neuron perceptron decision boundary Wp + b = 0 set to ZERO?

I am learning about artificial neural networks. I understand how the weights determine the slope of the (orthogonal) decision boundary and how the bias shifts that decision boundary, much like a line. ...
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Support Vector Machine with Perceptron Loss

Typical support vector classifier uses the following optimization procedure: $$\min ||w||^2 + C\sum_{i=1}^N \zeta_i$$ $$y_i(w^Tx_i+b) \geq 1 - \zeta_i$$ $$\zeta_i \geq 0$$ This hinge loss setup ...
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Why do my training losses go up?

I am new to Machine Learning and Tensorflow. For one of my courses, I need to train an MLP for the xor gate. But my losses somehow go up each epoch, which confuses me and I must admit that I ran out ...
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218 views

Convergence speed of perceptron algorithm

I was reading the convergence proof for the perceptron algorithm. It says under the assumption that there are some $R$, $\theta^*$ with $|\theta^*| = 1$ and $\gamma > 0$, such that $y_t(x_t\cdot \...
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Can a unit-step function introduce non-linearity if used in the hidden nodes?

I have a follow up from this question. From the answers in that question, it's obvious that at minimum, a shallow network (only has a single hidden layer), as defined in the "Universal Approximation ...
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linear perceptron algorithm with 3 training samples

So I am working on a linear perceptron algorithm problem that has 3 training samples. (2D space) ...
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Gradient descent with Binary Cross-Entropy for single layer perceptron

I'm implementing a Single Layer Perceptron for binary classification in python. I'm using binary Cross-Entropy loss function and gradient descent. The gradient descent is not converging, may be I'm ...
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How are the weights of a single layer perceptron updated given the misclassification of a point?

Here is some data separated by decision line The equation of decision line/boundary is $x_1 -3x_2 + 3 = 0$. Therefore $w_1 = 1, w_2 = -3$ and, if $w_0 = -1$, then the bias $\theta = -3$. Now, I have ...
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Why does Perceptron use L1 norm as its error function?

Usually, people use L2 norm as a machine learning error function. per wiki the error function for a Perceptron model is ${\displaystyle {\frac {1}{s}}\sum _{j=1}^{s}|d_{j}-y_{j}(t)|}$ why is that?
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Rule of thumb Overfit in a MLP or is it possible with N = 135

I want to say ahead that I highly appreciate any Literature recommendation (Book, blogg, ...) besides ESL and ISL. My Question: Is it possible to train a 3 layer multilayer perceptron (mlp) in a ...
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How are the weights updated in the perceptron learning rule?

I'm considering a perceptron model. I know that when feeding observations from the training dataset to the model, if the model correctly classifies the input, then the weights for this input will not ...
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Why perceptron is linear classifier?

It is said that perceptron is linear classifier, but it has a non-linear activation function f = 1 if wx - b >= 0 and f = 0 otherwise If i will use some non-linear function on linear combination of ...
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Training Perceptrons with Backprop

Is it possible to train a simple perceptron with a threshold activation function such as this one: https://en.wikipedia.org/wiki/Perceptron with Backpropagation instead of the perceptron rule? is it ...
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No need for bias term if data is standardised? Linear classification models

For linear classification models, e.g. perceptron, bias term allows to move separating hyperplane away from origin. If data is scattered around the zero does that mean that we don't need bias term?
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Choosing perceptron weights to achieve 0% error

I'm really not sure what to do for this question, although I think I would be able to do q3 if I knew how to do q2
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Perceptron learning for non-linearly separable data

It is well known that perceptron learning will never converge for non-linearly separable data. This means that you cannot fit a hyperplane in any dimensions that would separate the two classes. Is it ...
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Neural Networks Perceptrons and MLPs

While studying as a newbie about Neural Networks I started as everyone from the basics (perceptrons, MLPs) then how backpropagation works before dive in to harder deep learning concepts. Now, I am ...
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Neural Network Weight Update

I posted this on Software Engineering and was told that it might be better here and on AI. I'm currently reading Artificial Intelligence: A Guide to Intelligent Systems by Michael Negnevitsky (3rd ...
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Can this network learn the XOR function?

Let's say I have the following constraints: The architecture is fixed (see image) (note that there are no biases) Activation function for the hidden layer is ReLU There's no activation function for ...
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No error in the training/validation dataset

Suppose I have a linearly separable dataset, divide into training and validation sets. Will a perceptron learned on the training dataset be guaranteed to have no error on the training dataset and on ...
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Derivation of Perceptron weight update formula

I've started out studying Machine Learning and am currently reading up about how a single perceptron works. From the wikipedia page, my understanding is as follows: suppose we have an input sample $\...