SVMs are basically linear regressions. Everything else you state is nonsense. In direct contrast to NNs (or heaven forbid optimessed SMA Xovers) SVMs are not as prone to overfitting due to their inherent linearity. In addition, SVMs generally outperform NNs in prediction errors.Quote from alexandermerwe:
SVMs is a very fancy way of using dummy variables in your usual linear regression, something that leads to excessive optimization.
I think you'll do better using a SMA optimized crossover.
Just IMO.
Alex