We view regularized learning of a function in a Banach space from its finite samples as an optimization problem. Within the framework of reproducing kernel Banach spaces, we prove the representer theorem for the minimizer of regularized learning schemes with a general loss function and a nondecreasing regularizer. When the loss function and the regularizer are differentiable, a characterization equation for the minimizer is also established.

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We prove a second fundamental theorem in the sense of Nevanlinna's theory of meromorphic functions replacing the constants$a$in $$\bar N$$ ($r, f, a$) by rational functions$R$with$R(∞)=a$. The key argument is Ahlfors's second fundamental theorem from his theory of covering surfaces.