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Questions tagged [perceptron]

26 questions
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3 answers

what is difference between multilayer perceptron and multilayer neural network?

When do we say that a artificial neural network is a multilayer Perceptron? And when do we say that a artificial neural network is a multilayer? Is the term perceptron related to learning rule to update the weights? Or It is related to neuron units?
Mohammad
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2 answers

Z-score relations to perceptrons

I am learning about preceptrons and my professor noted that z-scores are a commmon pre-processing step to normalizing input variables. Following this, I am having trouble understanding why z-scores are useful when training a preceptron? My current…
user40759
4
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1 answer

What is a homogenous half-space?

I can't think of any definition for half-space that would involve some sort of quantity not being homogenous. This term is used in the paper Robust Concepts and Random Projection in the following paragraphs: The next problem we consider is learning…
djechlin
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1 answer

Why do people insist to use the term "multilayer perceptron" instead of "multilayer perceptron network"?

The perceptron model describes a linear classifier. Often people use the term "multilayer perceptron" to describe a feedforward neural network that uses perceptrons. This terminology simply sounds wrong to me, since this type of network does not…
Moon
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3
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1 answer

Are Perceptrons the neural network equivalence of Linear and Logistic Regression?

am I right in the assumption that both linear and logistic regression algorithms can be represented as the simplest form of neural networks,a perceptron, which consists of a two layers, an Input and an output, that combines all the input each with…
Raed Tabani
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Square or circle neural network detection

I am trying to use a simple perceptron to recognize if there is a square or a circle on an image. The images I generated are 300x300 px and I am having issues training the network since the images are large and I seem to run out of memory after a…
Martin Gottweis
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3
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1 answer

Confusion regarding terminology surrounding perceptron learning

I am having trouble understanding the terminology with perceptron learning. Is my current understanding correct? Let's say I have some data that classifies what type of flower a particular flower is. And let's say the factors taken into…
user40759
2
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1 answer

How do I prove that the Perceptron bound for mistakes is tight?

How do I prove that the Perceptron bound for mistakes is tight? I need to prove that for any amount of given data points, the total amount of updates (mistakes) that the algorithm will make is $\frac{1}{\gamma ^2}$, where $\gamma$ is the margin. I…
Robert H
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Representational power of Neural Neural Networks without a bias term

In a fully connected Neural Network, each perceptron has it's bias term $b$ which is learnt. Often (example, in Linear/ Logistic Regression), when we don't want to treat this bias term explicitly, we add an extra "constant" feature $x_0 = 1$ to each…
Harry
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Proof of perceptron convergence theorem for ZERO threshold?

The generalized perceptron convergence theorem is for a defined threshold T. When you do the maths it all comes to an upper bound and a lower bound. The lower bound looks like this! Therefore $$ (x^{\ast})^T x(k) > k \delta. \qquad\qquad…
elou
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Question about simple perceptron code

I'm reading through Sebastian Raschka's Python Machine Learning, and I see something confusing that is not explained in the text. In the code on this…
tmsimont
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Winnow versus Perceptron - Why adding irrelevant features increases L2(X) but not L∞(X)?

I saw here: http://www.cs.cmu.edu/~ninamf/ML11/lect0906.pdf Intuitively, if “n” is large but most features are irrelevant (i.e. target is sparse but examples are dense), then Winnow is better because adding irrelevant features increases L2(X) but…
Lee
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Teaching perceptrons colors?

I am learning about artificial neural networks and I've decided to go with perceptrons. I already made a sample program that can learn based on the learning data, but when I tried to make it recognize the color green it stopped working. The learning…
NoahGav
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Draw the decision boundary of an neural network

I'm really confused when it comes to what the difference between bias and threshold is. I have read that they are basically the same thing and that they serve the same purpose. Being on opposite sides of the equation, though, they are "negatively…
kim120
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Can the perceptron classifier achieve perfect accuracy, on any data set?

I was thinking. Since any data can become linearly seprabale through kernel methods, meaning there is a dimension where this data is linearly seprable, so feed this processed data set into the perceptron algorithm and then it might get to 100…
mathkid
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