Can we use SVM for feature selection?
SVM is a classification approach based on finding the optimal hyperplan. There is many way to use SVM for feature selection problem: Use SVM to compute the objective function (Classification accuracy rate) and attempt to select the subset of feature that optimizing the classification accuracy rate.
Why are there different kernels in SVM?
“Kernel” is used due to set of mathematical functions used in Support Vector Machine provides 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 transformed to a linear equation in a higher number of dimension spaces.
How do you find the optimal hyperplane in SVM?
If I have a margin delimited by two hyperplanes (the dark blue lines in Figure 2), I can find a third hyperplane passing right in the middle of the margin. Finding the biggest margin, is the same thing as finding the optimal hyperplane.
How are feature selection and kernels used in SVM?
The main objective in SVM is to find the optimal hyperplane to correctly classify between data points of different classes (Figure 2). The hyperplane dimensionality is equal to the number of input features minus one (eg. when working with three feature the hyperplane will be a two-dimensional plane).
Why are support vectors important in the SVM?
Support vectors are special because they are the training points that define the maximum margin of the hyperplane to the data set and they therefore determine the shape of the hyperplane. If you were to move one of them and retrain the SVM, the resulting hyperplane would change.
How to define hyperplanes in support vector machines?
, instead of just the 3 circled points at the tail ends of the support vectors. Define the hyperplanes H such that: = –1 d+ = the shortest distance to the closest positive point d- = the shortest distance to the closest negative point The margin (gutter) of a separating hyperplane is d+ + d–.