6.16 (**) Consider a parametric model governed by the parameter vector w together with a data set of input values X1, ..., XN and a nonlinear feature mapping Q(x). Suppose that the dependence of the error function on w takes the form J(w) = f(wTº(x1), ..., wTº(xn)) + g(wIw) W W W T W (6.97) where g() is a monotonically increasing function. By writing w in the form N W = ape(x)+w| (6.98) n=1 show that the value of w that minimizes J(w) takes the form of a linear combination of the basis functions °(xn) for n = 1, ...,N.
