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Classification by Sparse Neural Networks
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SYSNO ASEP 0485611 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Classification by Sparse Neural Networks Tvůrce(i) Kůrková, Věra (UIVT-O) RID, SAI, ORCID
Sanguineti, M. (IT)Zdroj.dok. IEEE Transactions on Neural Networks and Learning Systems - ISSN 2162-237X
Roč. 30, č. 9 (2019), s. 2746-2754Poč.str. 9 s. Jazyk dok. eng - angličtina Země vyd. US - Spojené státy americké Klíč. slova Binary classification ; Chernoff–Hoeffding bound ; dictionaries of computational units ; feedforward networks ; measures of sparsity Vědní obor RIV IN - Informatika Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) CEP GA15-18108S GA ČR - Grantová agentura ČR GA18-23827S GA ČR - Grantová agentura ČR Způsob publikování Omezený přístup Institucionální podpora UIVT-O - RVO:67985807 UT WOS 000482589400015 EID SCOPUS 85071708566 DOI 10.1109/TNNLS.2018.2888517 Anotace 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. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2020 Elektronická adresa http://dx.doi.org/10.1109/TNNLS.2018.2888517
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