Hi all
I'm trying to work out a general convergence framework for Machine Learning
Algorithms which utilize iterations for optimization. For now, I can think
of three kinds of convergence functions which might be useful.
1. converge(data, modelBeforeIteration, modelAfterIteration)
2. converge(data, modelAfterIteration)
3. converge(data, modelBeforeIteration, iterationState, modelAfterIteration)
where iterationState is some state computed while performing the iteration.
Algorithm implementation would have to support all three of these, if
possible. While specifying the {{Predictor}}, user would implement the
Convergence class and override these methods with their own implementation.
Any feedback and design suggestions are welcome.
Regards
Sachin Goel
