- The authors approach is only applicable for problems that are small or medium scale. Truly large problems will overwhelm current LP-solvers. - The authors only applied their method on peculiar types of machine learning applications that were already used for testing boolean classifier generation. It is unclear whether the method could lead to progress in the direction of cleaner machine learning methods for standard machine learning tasks (e.g. MNIST). Questions: - How where the time limits in the inner and outer problem chosen? Did larger timeouts lead to better solutions? - It would be helpful to have an algorithmic writeup of the solution of the pricing problem. - SVM gave often good results on the datasets. Did you use a standard SVM that produced a linear classifier or a Kernel method? If the former is true, this would mean that the machine learning tasks where rather easy and it would be necessary to see results on more complicated problems where no good linear separator exists. Conclusion: I very much like the paper and strongly recommend its publication. The authors propose a theoretically well grounded approach to supervised classifier learning. While the number of problems that one can attack with the method is not so large, the theoretical (problem formulation) and practical (Dantzig-Wolfe solver) contribution can possibly serve as a starting point for further progress in this area of machine learning.
- The authors approach is only applicable for problems that are small or medium scale. Truly large problems will overwhelm current LP-solvers.