Machine Learning Software

The following code is copyrighted under the GNU GPL. It does not come with any warranty of any kind.

Learning the graph


This software learns the best of (finitely many) graphs for semi--supervised learning.

Combining Graph Laplacians for Semi--Supervised Learning.


The Matlab code is available here. The code also includes implementations of a few image transformations such as tangent distances.

Learning the kernel continuously


With this method, we can learn convex combinations of, say, Gaussian kernels with parameters in a given range.

Learning Convex Combinations of Continuously Parameterized Basic Kernels.

A DC-Programming Algorithm for Kernel Selection.

Code (using DC programming).

Multi-task feature learning


This is a method for learning multiple tasks simultaneously, assuming that they share a set of common features. It is based on regularizing the spectrum of the tasks matrix. An example of such a method is regularization with the trace norm.

Multi-Task Feature Learning.

Convex Multi-Task Feature Learning.

A Spectral Regularization Framework for Multi-Task Structure Learning.


School data [H. Goldstein. Multilevel modelling of survey data. The Statistician, 40:235, 1991].

The data set from [Lenk et al.] is not in the public domain. Please request it from the authors of that paper.

Note: to use a nonlinear kernel, one can run the above code on the Gram matrix after a preprocessing with a Gram-Schmidt or Cholesky decomposition (see Convex Multi-Task Feature Learning).

Accelerated optimization for composite regularizers


This optimization method solves regularization problems with regularizers R(Bx) where R is a nonsmooth function, with an easy to compute proximity operator, and B is a linear map. It uses a combination of proximal methods with acceleration.

Efficient First Order Methods for Linear Composite Regularizers.


Last modified: Thu Apr 7 15:11:06 CDT 2011