Rademacher complexity and empirical Rademacher complexity are used to provide upper bound on the loss of solving an learning problem. That seems to imply that Rademacher complexity and empirical Rademacher complexity can't be negative. But from their definitions, I can't figure out if they are indeed always nonnegative. So what is the truth? Thanks.For their definitions, I refer to  http://www.cc.gatech.edu/~ninamf/ML11/lect1115.pdf. 
Are (empirical) Rademacher complexity always positive?
machine learning