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Probabilistic Bounds for Binary Classification of Large Data Sets
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SYSNO ASEP 0503127 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Probabilistic Bounds for Binary Classification of Large Data Sets Author(s) Kůrková, Věra (UIVT-O) RID, SAI, ORCID
Sanguineti, M. (IT)Source Title Recent Advances in Big Data and Deep Learning. - Cham : Springer, 2020 / Oneto L. ; Navarin N. ; Sperduti A. ; Anguita D. - ISSN 2661-8141 - ISBN 978-3-030-16840-7 Pages s. 309-319 Number of pages 11 s. Publication form Print - P Action INNSBDDL 2019: INNS Big Data and Deep Learning /4./ Event date 16.04.2019 - 18.04.2019 VEvent location Sestri Levante Country IT - Italy Event type WRD Language eng - English Country CH - Switzerland Keywords Binary classification ; Approximation by feedforward networks ; Concentration of measure ; Azuma-Hoeffding inequality Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects GA18-23827S GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 DOI 10.1007/978-3-030-16841-4_32 Annotation A probabilistic model for classification of task relevance is investigated. Correlations between randomly-chosen functions and network input-output functions are estimated. Impact of large data sets is analyzed from the point of view of the concentration of measure phenomenon. The Azuma-Hoeffding Inequality is exploited, which can be applied also when the naive Bayes assumption is not satisfied (i.e., when assignments of class labels to feature vectors are not independent). Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2021
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