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- W79652904 abstract "AbstractLimitations of capabilities of one-hidden-layer networks are investigated. It is shown that for networks with Heaviside perceptrons as well as for networks with kernel units used in SVM, there exist large sets of d-variable functions which cannot be tractably represented by these networks, i.e., their representations require numbers of units or sizes of weighs depending on d exponentially. Our results are derived using the concept of variational norm from nonlinear approximation theory and the concentration of measure property of high dimensional Euclidean spaces.Keywordsmodel complexity of neural networksone-hidden-layer networkshighly-varying functionstractability of representations of multivariable functions by neural networks" @default.
- W79652904 created "2016-06-24" @default.
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- W79652904 date "2014-01-01" @default.
- W79652904 modified "2023-10-13" @default.
- W79652904 title "Representations of Highly-Varying Functions by One-Hidden-Layer Networks" @default.
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- W79652904 doi "https://doi.org/10.1007/978-3-319-07173-2_7" @default.
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