Using SVM Seperating Plane Distance as a Feature¶

Support Vector Machines (SVM) are one of my favorite machine learning algorithms. I have decided to make a set of blog posts to explore tricks for dealing with SVMs. Usually SVMs are employed as black box binary classifiers. In this post we are going to explore using the underlying SVMs representation to generate features to use as input for further calculation (for example as an input to other classifiers).

Lattice SVM¶

A support vector machine (SVM) is a classifier that attempts to find a maximum margin linear separator for different classes in a very high dimensional implicit feature space. The feature space is usually not explicitly calculated but is instead accessed via a kernel function which provides the effective dot product in the feature space, this has the advantage that we can deal with very large implicit feature spaces this way. In fact the dimensionality of the implicit feature space of most commonly used SVM variants is usually quoted as being infinite, for example the Gaussian kernel is one example. But the high effective feature dimensionality still comes with a high computational cost, we must somehow deal with an N by N matrix of similarities relating all of our training points to each other (the matrix of kernelized "feature dot products").

Asymptotic Labs (Posts about SVM)

Using SVMs as Feature Extractors

Using SVM Seperating Plane Distance as a Feature¶

Lattice SVM

Lattice SVM¶