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I am currently studing svm and was wondering what the application of svm`s with linear kernel is. In my opinion it must be something applied to solving a linear optimization problem. Is this correct?
I appreciate your answer!
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Gaussian Radial Basis Function (RBF) It is one of the most preferred and used kernel functions in svm.
Linear Kernel is used when the data is Linearly separable, that is, it can be separated using a single Line. It is one of the most common kernels to be used. It is mostly used when there are a Large number of Features in a particular Data Set.
“Kernel” is used due to a set of mathematical functions used in Support Vector Machine providing the window to manipulate the data. So, Kernel Function generally transforms the training set of data so that a non-linear decision surface is able to transform to a linear equation in a higher number of dimension spaces.
Usually linear and polynomial kernels are less time consuming and provides less accuracy than the rbf or Gaussian kernels. The k cross validation is used to divide the training set into k distinct subsets. Then every subset is used for training and others k-1 are used for validation in the entire trainging phase.
One more thing to add: linear SVM is less prone to overfitting than non-linear. And you need to decide which kernel to choose based on your situation: if your number of features is really large compared to the training sample, just use linear kernel; if your number of features is small, but the training sample is large, you may also need linear kernel but try to add more features; if your feature number is small (10^0 - 10^3), and the sample number is intermediate (10^1 - 10^4), use Gaussian kernel will be better.
As far as I know, SVM with linear kernel is usually comparable with logistic regression .
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Linear kernel has some advantages but probably (in my opinion) the most significant one is the fact that generally is way faster to train in comparison with non-linear kernels such as RBF.
If your dataset size is in terms of gigabytes, you would see that the training time difference is huge (minutes vs. hours).
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