Support vector machines
1 min read
Built upon Simple Intuitive maximal margin classifier
MMC requires classes to be separable by a linear boundary
SVC solves a broader case
SVC uses a soft margin to solve inseparable cases
a few samples is on the incorrect side, but the general public is better
SVM is a further extension and can solve nonlinear boundaries
SVM is able to create nonlinear boundaries
One way is enlarging the feature space
A better way is using a kernel
A kernel is simply a function
Instead of the inner product of features, we apply a function to it, and call this function a kernel
This essentially means fitting an SVC in a higher dimensional space
Using a kernel is computationally efficient
SVM is connected to logistic regression, their loss functions are very similar
Also kernels can be used with other methods, too. Although they are used with SVMs mostly
You can use SVMs for more than 2 classes.

one vs one Create SVMs for all 2pairs of classes. In the end a sample belongs to the class to which it is most frequently assigned

one vs all
January 16, 2020