Python: Least Squares Support Vector Machine (LS-SVM) I'm looking for a Python package for a LS-SVM or a way to tune a normal SVM from scikit-learn to a Least-Squares Support Vector Machine for a classification problem. 

The goal of a SVM is to maximize the margin while softly penalizing points that lie on the wrong side of the margin boundary. The function that is used is a Quadratic Programming (OP) problem. 

A LS-SVM which defines a least squares cost function and replaces the inequality contraints with equality constraints and is a Linear Programming (LP) problem.

Wikipedia: LS-VSM

Does someone ever tried something like that and can help me?
There is a package called FukuML.

In their description (Version 0.4.1) they write:


Support Vector Machine


Primal Hard Margin Support Vector Machine Binary Classification Learning Algorithm
Dual Hard Margin Support Vector Machine Binary Classification Learning Algorithm
Polynomial Kernel Support Vector Machine Binary Classification Learning Algorithm
Gaussian Kernel Support Vector Machine Binary Classification Learning Algorithm
Soft Polynomial Kernel Support Vector Machine Binary Classification Learning Algorithm
Soft Gaussian Kernel Support Vector Machine Binary Classification Learning Algorithm
Polynomial Kernel Support Vector Machine Multi Classification Learning Algorithm
Gaussian Kernel Support Vector Machine Multi Classification Learning Algorithm
Soft Polynomial Kernel Support Vector Machine Multi Classification Learning Algorithm
Soft Gaussian Kernel Support Vector Machine Multi Classification Learning Algorithm
Probabilistic Support Vector Machine Learning Algorithm
Least Squares Support Vector Machine Binary Classification Learning Algorithm
Least Squares Support Vector Machine Multi Classification Learning Algorithm
Support Vector Regression Learning Algorithm



As you can see, they have the feature you're after.