Classification by Sparse Neural Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F19%3A00485611" target="_blank" >RIV/67985807:_____/19:00485611 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/TNNLS.2018.2888517" target="_blank" >http://dx.doi.org/10.1109/TNNLS.2018.2888517</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TNNLS.2018.2888517" target="_blank" >10.1109/TNNLS.2018.2888517</a>
Alternative languages
Result language
angličtina
Original language name
Classification by Sparse Neural Networks
Original language description
The choice of dictionaries of computational units suitable for efficient computation of binary classification tasks is investigated. To deal with exponentially growing sets of tasks with increasingly large domains, a probabilistic model is introduced. The relevance of tasks for a given application area is modeled by a product probability distribution on the set of all binary-valued functions. Approximate measures of network sparsity are studied in terms of variational norms tailored to dictionaries of computational units. Bounds on these norms are proven using the Chernoff–Hoeffding bound on sums of independent random variables that need not be identically distributed. Consequences of the probabilistic results for the choice of dictionaries of computational units are derived. It is shown that when a priori knowledge of a type of classification tasks is limited, then the sparsity may be achieved only at the expense of large sizes of dictionaries.
Czech name
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Czech description
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Classification
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)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Neural Networks and Learning Systems
ISSN
2162-237X
e-ISSN
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Volume of the periodical
30
Issue of the periodical within the volume
9
Country of publishing house
US - UNITED STATES
Number of pages
9
Pages from-to
2746-2754
UT code for WoS article
000482589400015
EID of the result in the Scopus database
2-s2.0-85071708566