It is my understanding that you want to implement stacking ensemble technique.
In your case, train Gaussian SVM, medium decision tree and linear robust regression models and get the predictions from them. Then feed these predictions as input features to the second layer of another linear model or SVM. This is how you can stack your multiple regression type ML model.
For the first layer input you could experiment by playing with same or different features. By doing this you will know which feature is important for which model and how it is affecting the accuracy.
In stacked ensemble you have to give prediction as features to the second layer input. So, there is no need of weights of first layer.
You could incorporate classification model in second layer if you want. In that case, the classification model will take predictions of first layer as input.