Cutting-plane Structured SVM in Julia, solving the Primal QP of the 1-slack formulation
This implementation has the following advantages over SVM-struct-matlab:
- Constraint caching (reuse full inference results)
- Parallelization of the inference step (one process per example)
- Additional positivity constraints on arbitrary coordinates of w
The two first points are very beneficial in settings were the inference task takes most of the time.
See SSVMjulia-tutorial.ipynb for a tutorial in IJulia notebook.
Joachims, T., Finley, T., & Yu, C. N. J. (2009). Cutting-plane training of structural SVMs. Machine Learning, 77(1), 27-59.