Machine Learning at the Edge
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00335642" target="_blank" >RIV/68407700:21230/19:00335642 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/book/9780128094488/software-engineering-for-embedded-systems#book-description" target="_blank" >https://www.sciencedirect.com/book/9780128094488/software-engineering-for-embedded-systems#book-description</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/C2015-0-06188-3" target="_blank" >10.1016/C2015-0-06188-3</a>
Alternative languages
Result language
angličtina
Original language name
Machine Learning at the Edge
Original language description
In this chapter we start by introducing machine learning (ML). We explain the terminology such as supervised and unsupervised ML. We explain the ML tasks called classification and regression. Then we introduce algorithms such as nearest neighbor or support vector machine (SVM), and speak about decision trees and in reference to an example of decision trees we explain ensemble techniques as well as boosting and bagging techniques.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
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
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Software Engineering for Embedded Systems
ISBN
978-0-12-809448-8
Number of pages of the result
53
Pages from-to
549-601
Number of pages of the book
645
Publisher name
Elsevier B.V.
Place of publication
New York
UT code for WoS chapter
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