Most $L^p$-type universal approximation theorems guarantee that a given machine learning model class $mathscr{F}subseteq C(mathbb{R}^d,mathbb{R}^D)$ is dense in $L^p_{mu}(mathbb{R}^d,mathbb{R}^D)$ for any suitable finite Borel measure $mu$ on $mathbb{R}^d$. Unfortunately, this means that the models approximation quality can rapidly degenerate outside some compact subset of $mathbb{R}^d$, as any such measure is largely concentrated on some bounded subset of $mathbb{R}^d$. This paper proposes a generic solution to this approximation theoretic problem by introducing a canonical transformation which upgrades $mathscr{F}$s approximation property in the following sense. The transformed model class, denoted by $mathscr{F}text{-tope}$, is shown to be dense in $L^p_{mu,text{strict}}(mathbb{R}^d,mathbb{R}^D)$ which is a topological space whose elements are locally $p$-integrable functions and whose topology is much finer than usual norm topology on $L^p_{mu}(mathbb{R}^d,mathbb{R}^D)$; here $mu$ is any suitable $sigma$-finite Borel measure $mu$ on $mathbb{R}^d$. Next, we show that if $mathscr{F}$ is any family of analytic functions then there is always a strict gap between $mathscr{F}text{-tope}$s expressibility and that of $mathscr{F}$, since we find that $mathscr{F}$ can never dense in $L^p_{mu,text{strict}}(mathbb{R}^d,mathbb{R}^D)$. In the general case, where $mathscr{F}$ may contain non-analytic functions, we provide an abstract form of these results guaranteeing that there always exists some function space in which $mathscr{F}text{-tope}$ is dense but $mathscr{F}$ is not, while, the converse is never possible. Applications to feedforward networks, convolutional neural networks, and polynomial bases are explored.
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