 Thus, the use of the kernel has allowed the movement from a finite dimensional parameter space formula_98 to a possibly infinite dimensional feature represented by a kernel formula_91 by instead performing the recursion on the space of parameters formula_100, whose dimension is the same as the size of the training dataset.In general, this is a consequence of the representer theorem.Online convex optimization (OCO)  is a general framework for decision making which leverages convex optimization to allow for efficient algorithms.