Approximative Compactness of Linear Combinations of Characteristic Functions

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00524108" target="_blank" >RIV/67985807:_____/20:00524108 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.jat.2020.105435" target="_blank" >http://dx.doi.org/10.1016/j.jat.2020.105435</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.jat.2020.105435" target="_blank" >10.1016/j.jat.2020.105435</a>

Result language
angličtina
Original language name
Approximative Compactness of Linear Combinations of Characteristic Functions
Original language description
Best approximation by the set of all n-fold linear combinations of a family of characteristic functions of measurable subsets is investigated. Such combinations generalize Heaviside-type neural networks. Existence of best approximation is studied in terms of approximative compactness, which requires convergence of distance-minimizing sequences.
Czech name
—
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
—
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/GA18-23827S" target="_blank" >GA18-23827S: Capabilities and limitations of shallow and deep networks</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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