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 lattices)

Lattice SVM

Lattice SVM¶