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Results tagged with svm
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user 755
Questions about Support Vector Machines. SVMs are supervised learning models used for classification and regression tasks.
0
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Accepted
SVM with different length features
Then apply the SVM on the feature vector of length 147. SVM doesn't care where the individual numbers came from.
Code is off-topic on this site. …
D.W.♦
- 166k
1
vote
How to find max margin for non-separable SVM?
Algorithms for finding a maximum-largin linear separator are described in the literature on SVMs (that's exactly what you need to do to train a linear SVM). … In other words, you can map the points to the new space, then can take those mapped points and train a linear SVM on them. That's conceptually how to do it. …
D.W.♦
- 166k
1
vote
Accepted
Why doesn't this derivation of the margin in a SVM give the correct result?
The first line of your equation for $d$ is incorrect. It should be
$$d = \left\|\vec w\frac{\vec x_+\cdot\vec w}{\vec w\cdot\vec w} - \vec w\frac{\vec x_-\cdot\vec w}{\vec w\cdot\vec w}\right\|.$$
…
D.W.♦
- 166k
0
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Accepted
What is a Black Box attack against Machine Learning algorithms?
This is known as a "model extraction attack", and there's a line of research in the computer security literature on this problem. For instance, you might look at the seminal paper by Papernot et al o …
D.W.♦
- 166k
2
votes
Overfitting in Machine Learning Algorithms
Yes, they can overfit too. Overfitting is especially a risk when the number of features is much larger than the number of samples in the training set.
D.W.♦
- 166k
1
vote
Accepted
SVM with a priori information about class probabilities
SVM doesn't take into account that prior knowledge about the distribution of the classes, so if you want a classifier that takes advantage of that, you'll need a different classifier. …
D.W.♦
- 166k
1
vote
Kernel Perceptron vs Polynomial Perceptron
A SVM with a polynomial kernel is a SVM classifier.
A kernel perceptron is a perceptron classifier, or in other words, a neural net.
A SVM is quite different from a neural net. … -- a SVM with a linear kernel is similar to a single-layer perceptron classifier, in case that's what you were thinking of.) …
D.W.♦
- 166k
3
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How to tackle different sample size in the training set in SVM
A SVM classifier requires a fixed-length feature vector, i.e., all feature vectors must have the same length. There are multiple solutions:
Pad out the strings to fixed length. … I don't think a SVM is the right tool for that job -- this sounds like an XY problem. …
D.W.♦
- 166k
1
vote
k-means clustered data: how to label newly incoming data
This is not answerable without knowing something about the data itself and where it comes from and what it means. But it sounds like you're trying to do something awfully dubious.
It sounds like you …
D.W.♦
- 166k
0
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Classification accuracy based on top 3 most likely classifications
This is called top-3 accuracy. See https://stats.stackexchange.com/q/95391/2921, https://stackoverflow.com/q/37668902/781723, https://stats.stackexchange.com/q/156471/2921. Sure, you can use it. Wh …
D.W.♦
- 166k
1
vote
Accepted
SVMs - Fat (Margin) Boundary: Why is $\max\frac1{||\theta||}=\min \frac{ ||\theta||^2}{2}$?
Minimizing $\|\theta\|$ is equivalent to minimizing $\|\theta\|^2/2$, in the sense that the minimum is achieved for the same value of $\theta$. Since our goal is primarily to find $\theta$, this subs …
D.W.♦
- 166k