I have a data classification problem and I'm wondering what is the best machine learning approach to use for the particular constraints of my problem.
My constraints are as follows: - the data points are not linearly separable (in the original space) - I can generate as many training samples of either positive or negative labels - I would like to minimize the number of false positives (i.e. negative samples being wrongly classified as positives) - the classification speed needs to be very fast
I am currently using an SVM but it's not giving me result, particularly on speed. I posted a related question about my problem here: SVM model selection for datasets with sharp corners I'm still waiting for an answer on that, but I started questioning whether I'm even taking the right approach, which is why I've posted this as a separate question.
In case it's relevant, my problem is to use a classifier (or other ML method) as a fast approximate collision detection method (i.e. faster than doing exact collision detection)