Learning Functional Transduction: S.I. Contents
–Neural Information Processing Systems
We propose below the proofs of the results presented in the main text. Most of the arguments are adapted from the development proposed in (Zhang, 2013) which goes beyond real or complex-valued RKBS developed in (Zhang et al., 2009; Song et al., 2013) to develop the notion of vector-valued RKBS. In addition, we note that assumptions regarding the properties of the RKBS of interests such as uniform Fréchet differentiability and uniform convexity have been further relaxed in other works (Xu and Ye, 2019; Lin et al., 2022) but are here sufficient for our discussion since they guarantee the unicity of a semi-inner product x.,.yB compatible with the norm ||.||B (Giles, 1967). S.1.1 Theoretical results Theorem 1 Theorem 1 gathers for the sake of compactness the definition of a vector-valued reproducing kernel Banach space with the properties of existence and unicity of the kernel K. Proof. For any v PV and u PU, the mapping OÞÑ xOpvq,uyU is a bounded linear form in LpBq.
Neural Information Processing Systems
Apr-30-2026, 04:22:35 GMT