https://github.com/iglesias/netpred

Structured learning with SVMs in C++ and Python
https://github.com/iglesias/netpred

hidden-markov-model machine-learning network-science probabilistic-graphical-models support-vector-machines

Last synced: 24 days ago
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Structured learning with SVMs in C++ and Python

• Host: GitHub
• URL: https://github.com/iglesias/netpred
• Owner: iglesias
• Created: 2013-03-21T22:51:39.000Z (almost 12 years ago)
• Default Branch: master
• Last Pushed: 2023-04-20T07:55:50.000Z (over 1 year ago)
• Last Synced: 2024-10-15T22:48:36.950Z (2 months ago)
• Topics: hidden-markov-model, machine-learning, network-science, probabilistic-graphical-models, support-vector-machines
• Language: OpenEdge ABL
• Homepage:
• Size: 3.21 MB
• Stars: 3
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
netpred

=======

Structured SVMs applied to label sequence learning (the so-called HM-SVM) and

graph learning (or grid CRFs in particular). I am using

this code to carry out some experiments during my final degree project. A lot

of inspiration has been drawn from [pystruct](http://github.com/amueller/pystruct)

by Andreas Mueller for the graph learning part. The HM-SVM implementation in

Shogun I am using here is based on the Matlab code by Gunnar Raetsch and Georg

Zeller [available at mloss](http://mloss.org/software/tags/hmsvm/). The code

makes use of [Shogun](http://shogun-toolbox.org)'s structured learning framework

([+ info](https://iglesiashogun.wordpress.com/2012/05/22/first-weekly-report-gsoc-2012/)).

Requirements

============

You definetely need Shogun and Swig. Also, you need to compile Shogun with

directors enabled and at least target Python's modular interface. In addition,

with the current state you need to compile Shogun with Mosek support. This

dependency should be easy to remove though by using any of the bundle methods

for SSVMs present in Shogun.

There are a couple of subgradient methods implemented in the graph directory.

Using this, you do not need Mosek. I have only tested them in the

graph learning task, but in principle they should work fine for label

sequence learning as well.

Apart from Shogun dependencies, you need cvxopt for the linear programming

relaxation used to solve the argmax in graph learning.

Report and presentation

=======================

Feel free to reach out if you would like to read my final degree project (PFC in Spanish) report.

[Video](https://www.youtube.com/watch?v=Ti_Wivo9OWY) of a presentation during the Shogun Workshop 2013.