Questions tagged [perceptron]

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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 ...
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18 views

Perceptron and non linearly seprable data

I was asked in an interview question, can a perceptron classifier ever reach 100% accuracy on some kind of non linearly seprabale training data in 2D. I said that no it can't because the data is not ...
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Delta rule for binary step function

I have a question of understanding about the Delta Rule: $\Delta w_i = (y - \hat{y}) \times x_i$ Why does $x$ have to be multiplied again after the difference? If the input is $0$, the product of $w$...
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Perceptron : upper bound

Given the following theorem: $\textbf{Theorem (Perceptron)}:$ Let $S$ be a sequence of labeled examples consistent with a linear threshold function $w^T \cdot x > 0,$ where $w$ is a unit-length ...
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Artificial Neuron: question on definition and inhibited signals

This is an artificial neuron of a NOT function from Rojas's ML book. I have a question on it's behavior. It's my understanding that the neuron produces a signal if it's inputs $x_1$ summed are $\geq ...
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42 views

Mcculloch and Pitts Neurons: Is there errata in this article?

The paper A LOGICAL CALCULUS OF THE IDEAS IMMANENT IN NERVOUS ACTIVITY has the following description of neurons, which I question if there is errata or perhaps I need more information to understand ...
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1answer
25 views

Using a point as an input for a perceptron

Can a point be used as the input for a perceptron/neural net? The relationship between the two numbers that make up a 2D point does not affect the output, but does this not matter when the ...
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1answer
75 views

Is it possible to detect cats from dogs in image with single layer perceptron?

I want to make a simple application that input is an image and output must be 0 if image is dog and 1 if image is cat. Is it possible to detect cats from dogs in image with single layer perceptron?
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Learning a perceptron from stream data

I want to train a Perceptron using stochastic gradient rulefrom the stream data. I have very limited amount of memory and i can store only $N$ examples. Suppose my population consist of point as ...
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1answer
1k views

Single Layer Perceptron vs Multi Layer Perceptron

Why the single layer perceptron has a linear activation function while the Multi Layer Perceptron has a non-linear activation function ? What is the potential of the Multi Layer Perceptron respect of ...
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149 views

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 ...
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1answer
102 views

Perceptrons: Functions not learnable without bias

I am trying to determine which functions are not learnable without a bias when building a perceptron. The set of functions I need to evaluate is {NOT, OR, AND}. Could someone help interpret these ...
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(Percision & recall) Vs (Accuracy)? which one do I have to consider?

I am running several machine learning classifiers to predict something from my data. If I visualized the precision and recall tables as a result, is it enough to get clear idea about the proposed mdel?...
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Multiple Weighted Hyerplanes to Classify XOR

I learned in class that perceptrons cannot simulate the XOR operation. My professor told me that one fix was to left the points onto a higher dimensional space. That is pretty obvious, you just use ...
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1answer
61 views

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 ...
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1answer
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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 ...
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712 views

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 ...
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1answer
97 views

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. ...
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2k views

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 ...
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1answer
178 views

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 page: https://github.com/rasbt/python-machine-...
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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 ...
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1answer
2k views

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 ...
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287 views

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{...
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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 ...
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Teaching perceptrons colors? [closed]

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 ...