# Feature boosting via rescaling in logistic regression and linear SVMs

If I were expressing a problem in terms of binary features, all encoded as {0,1}, could I boost some features by encoding them as {0,2}? Would the effect change based on whether I used either of the approaches above (logistic regression or linear SVMs?) Any paper recommendations that would explain the mathematics?

No, those are linear methods, the results would be equivalent. Multiplying $$x$$ by two would be offset by halfing the coefficient estimate.