so that the information gain the difference of information entropy of the whole space, to the sum of information entropies of the two divided sub-spaces is maximum. For this the SVM algorithm produces more features with a kernel function that combines any two existing predictors to a new feature. . Like neural networks, SVMs can be used not only for classification, but also for regression.
Youre still looking at the open, high, low, and close of candles. Numerical predictors can be converted to events by dividing the number into separate ranges. In the literature you can find y also named label forex market open strategy or objective. Xn, y model Prediction :. This way we have a binary classifier with optimal separation of winning and losing samples. In my past life as computer game programmer, we used such methods in games for tasks like self-learning enemy intelligence.