Probabilistic Bounds for Binary Classification of Large Data Sets

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00503127" target="_blank" >RIV/67985807:_____/20:00503127 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-16841-4_32" target="_blank" >http://dx.doi.org/10.1007/978-3-030-16841-4_32</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-16841-4_32" target="_blank" >10.1007/978-3-030-16841-4_32</a>

Result language
angličtina
Original language name
Probabilistic Bounds for Binary Classification of Large Data Sets
Original language description
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).
Czech name
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Czech description
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Type
D - Article in proceedings
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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