-
- 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)
This package includes software from the following projects: - slycot (for solving Sylvester equation, see README.slycot).
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:
Initial Announcement on mloss.org.
- BibTeX Entry: Download
- Corresponding Paper BibTeX Entry: Download
- Supported Operating Systems: Agnostic
- Data Formats: Matlab, Numpy
- Tags: Kernel Learning
- Archive: download here
Comments
No one has posted any comments yet. Perhaps you'd like to be the first?
Leave a comment
You must be logged in to post comments.