All Questions
Tagged with perceptron classification
19 questions
0
votes
0
answers
14
views
Scaling for binary data features
My goal is a binary classification of a sports match between two players, particularly I am concerned with the probabilities of each player winning.
My current dataframe has feature values of [...
- 47
0
votes
1
answer
58
views
Is it possible to predict non-classifiable label using single layer perceptron and sigmoid function? (without using any perceptron library)
Imagine predicting BMI index like 1,2,3,4,5 and having weight and height as input. I know it can be easily done with other method. Also I have to use sigmoid function and I am really new to this. I ...
4
votes
1
answer
87
views
Why there are two different versions for batch perceptron algorithm?
In the book "Understanding Machine Learning, S. David Ben et al.", the authors describe the Batch Perceptron Algorithm as follows:
However, in the book "Python Machine Learning, ...
- 670
1
vote
0
answers
16
views
Can Perceptron and Naive Bayes classifier create a vertical decision boundary in a two-dimensional graph?
A decision boundary like in the picture.
1
vote
0
answers
160
views
How can I explain the difference in decision boundary and its reason in relation to the dimension of the hidden layer?
I got these 3 different plots of decision boundaries using 3 different parameters for hidden_layer_sizes of the MLPClassifier from sklearn on XOR gate.
...
- 21
2
votes
1
answer
244
views
How to resolve the perceptron dilemma for binary classification?
I have a following thought problem involving perceptron and binary classification that I wonder if anyone has thought about before. This is not from any textbook or reference, although I doubt I'm the ...
- 734
1
vote
0
answers
98
views
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 ...
- 111
2
votes
0
answers
166
views
5
votes
2
answers
3k
views
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 ...
- 71
1
vote
1
answer
276
views
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?
- 59
0
votes
0
answers
73
views
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
user243609
1
vote
1
answer
5k
views
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 ...
- 299
4
votes
1
answer
1k
views
Neural Networks - Difference between one dimensional layer vs multi-dimensional layer
Please take a look at these Neural Network architectures:
I can understand the architecture of Hidden Layer in Net-2 : you add 12 Neurons to your hidden layer...
What I can't understand is the 2-...
- 515
0
votes
1
answer
2k
views
SLP vs. MLP: Is my data linearly separable?
I implemented an artificial neural network using scikit neuralnetwork. As default configuration for my classification task I am using
10730 Datsets x 115 Features
1 Hidden Layer with 61 neurons
7 ...
- 103
1
vote
0
answers
544
views
Why use a restricted Boltzmann machine rather than a multi-layer perceptron?
I'm trying to understand the difference between a restricted Boltzmann machine (RBM), and a feed-forward neural network (NN). I know that an RBM is a generative model, where the idea is to reconstruct ...
- 7,129
1
vote
1
answer
619
views
Can adding an additional feature to a perceptron classifier make the results worse?
I am using perceptron to solve a classification problem.
I have a limited amount of features (26) and iterate through all possible combinations of them.
A combination of two features [feature_a, ...
- 13
22
votes
3
answers
16k
views
From the Perceptron rule to Gradient Descent: How are Perceptrons with a sigmoid activation function different from Logistic Regression?
Essentially, my question is that in multilayer Perceptrons, perceptrons are used with a sigmoid activation function. So that in the update rule $\hat{y}$ is calculated as
$$\hat{y} = \frac{1}{1+\exp(...
user39663
5
votes
2
answers
3k
views
Formula for decision boundary of a classifier (in order to visualize it)
I'm confused on how to plot decision boundary for classifiers.
For example, i'm working with perceptron. So, the formula for decision boundary(if I understand this correctly) is
...
- 161
2
votes
2
answers
5k
views
Perceptron overfitting?
I'm trying to judge the performance of my perceptron linear discriminant. In one instance I'm training on a sample size of 150 and on another I'm training on a sample size of 1500. I test both of ...
- 111