Hi, I am working on a project that involves classifying many 'situations' where there are some number of objects and the objects can be defined by the makeup and weighting of their parameters. There are probably 200-2000 parameters per object, 1 to 100 objects and hundreds of situations. The situations may all be very different from each other and are graded by an unknown process -- no scheme can be assumed. The goal is to generate new combinations of objects, where only objects above a grade threshold are selected and the combinations are derived from correlations between similar situations, grade them and so on. I've been looking at c++ libraries like Shark Machine Learning and Classias *sorry I can't include links yet, they are both at the tip top of google* I am having trouble understanding what approaches are most appropriate for the problem. I am new to this which I suppose is apparent in the terminology. I would really appreciate any thoughts on which, if any, of the approaches in those libraries would be appropriate? Ultimately I would like to try any approaches that would be appropriate to find the best results.