Probabilistic Lower Bounds for Approximation by Shallow Perceptron Networks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F17%3A00473964" target="_blank" >RIV/67985807:_____/17:00473964 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.neunet.2017.04.003" target="_blank" >http://dx.doi.org/10.1016/j.neunet.2017.04.003</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.neunet.2017.04.003" target="_blank" >10.1016/j.neunet.2017.04.003</a>

Result language
angličtina
Original language name
Probabilistic Lower Bounds for Approximation by Shallow Perceptron Networks
Original language description
Limitations of approximation capabilities of shallow perceptron networks are investigated. Lower bounds on approximation errors are derived for binary-valued functions on finite domains. It is proven that unless the number of network units is sufficiently large (larger than any polynomial of the logarithm of the size of the domain) a good approximation cannot be achieved for almost any uniformly randomly chosen function on a given domain. The results are obtained by combining probabilistic Chernoff-Hoeffing bounds with estimates of the sizes of sets of functions exactly computable by shallow networks with increasing numbers of units.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/GA15-18108S" target="_blank" >GA15-18108S: Model complexity of neural, radial, and kernel networks</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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