Feature: anomaly detection with One-Class LS-SVM

[lsorber]

Ideas:

1. Allow the user to supply a target y and then apply a separating affine transform before running One-Class LS-SVM so that similarity is measured in y-separating feature space instead of the given feature space.
2. Perhaps use the resulting OC-LS-SVM (1) to fit a robust LS-SVM by reweighting or excluding samples.

References:

1. Least squares one-class support vector machine
2. One-class LS-SVM with zero leave-one-out error