Description:

A collection of python code to perform research in optimization. The aim is to provide reusable components that can be quickly applied to machine learning problems.

Dependencies:

• python 2.5+
• cvxopt 1.0+ (for solving linear and quadratic programs)
• pythongrid (for using a cluster)
• cython 0.14.1 (for speeding up kernel computations)
• slycot (for solving Sylvester equation).

Description

The projects currently prototyped:

• learning the output kernel using block coordinate descent
• difference of convex functions algorithms for sparse classfication

Learning the output kernel using block coordinate descent

• The workhorse is kernelopt.py, which implements a framework for minimizing the invex function for learning the kernel on outputs.
• multiclass_demo.py gives usage examples.

Changes to previous version:

• Fixed missing multiclass module.
• Slycot sources no longer distributed, using github project instead.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: Matlab, Numpy

Tags: Kernel Learning

Archive: download here

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