2004
DOI: 10.1090/s0273-0979-04-01025-0
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Shannon sampling and function reconstruction from point values

Abstract: PreambleI first met René at the well-known 1956 meeting on topology in Mexico City. He then came to the University of Chicago, where I was starting my job as instructor for the fall of 1956. He, Suzanne, Clara and I became good friends and saw much of each other for many decades, especially at IHES in Paris.Thom's encouragement and support were important for me, especially in my first years after my Ph.D. I studied his work in cobordism, singularities of maps, and transversality, gaining many insights. I also … Show more

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Cited by 206 publications
(166 citation statements)
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“…The bound (3.10) estimates the regularization error [10]. It is only used for the proof of Corollary 3 below.…”
Section: Lemma 1 Let H Be a Hilbert Space And {ξmentioning
confidence: 99%
See 1 more Smart Citation
“…The bound (3.10) estimates the regularization error [10]. It is only used for the proof of Corollary 3 below.…”
Section: Lemma 1 Let H Be a Hilbert Space And {ξmentioning
confidence: 99%
“…To understand (1.3), following our previous studies on Shannon sampling [10,11], we define the sampling operator S x : H K → IR m associated with a discrete subset…”
mentioning
confidence: 99%
“…The first term in (1.11) is called the regularization error [19]. It can be expressed as a K-functional, since for any measurable function f : X → IR, there holds…”
Section: Quantity Depending On the Variance Of ρ)mentioning
confidence: 99%
“…For a given class of data, we show how to estimate, a priori, a suitable kernel and parameter subspace. Smale and Zhou (2004) have also studied the application of sampling theory and reproducing kernel Hilbert spaces to learning theory. They consider the least squares loss regression problem and construct probability estimates for the sampling error.…”
Section: Introductionmentioning
confidence: 99%