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Can I read Neural Network Approximation of Continuous Functionals and Continuous Functions on Compactifications on EtoBox?
Neural Network Approximation of Continuous Functionals and Continuous Functions on Compactifications by M.B. Stinchcombe is a Computer Science article available to read on EtoBox.
What is Neural Network Approximation of Continuous Functionals and Continuous Functions on Compactifications about?
This article characterizes the set of activation functions, bounded or unbounded, that allow feedforward network approximation of the continuous functions on the classic two-point compactification of R 1 . The characterization fails when the set of targets are continuous functions on the classic compactifications of R n , n Ն 2. Nonpolynomial, analytic activation functions with input-to-hidden weights in very limited sets allow approximation of continuous function over compact sets in R n , while even sigmoidal activation functions with weights in limited sets cannot approximate continuous functions on compactifications. The abstract structure foregrounded by compactification leads directly to possibility results for multi-layer networks and possibility results for neural networks in infinite dimensional settings.
Who reads Neural Network Approximation of Continuous Functionals and Continuous Functions on Compactifications?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- M.B. Stinchcombe
- Publisher
- Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0893-6080)
- Published
- 1999
- Language
- EN
- Field
- Computer Science (Physical Sciences)