机读格式显示(MARC)
- 000 01842cam a2200325 i 4500
- 008 200630t20222022enka b 001 0 eng d
- 020 __ |a 9781108832373 |q hardback
- 020 __ |z 9781108955959 |q ebook
- 040 __ |a UkCbUP |b eng |c UkCbUP |e rda
- 050 _4 |a QA76.583 |b .S66 2022
- 082 04 |a 005.758 |2 23/eng/20211019
- 099 __ |a CAL 022022039582
- 100 1_ |a Guo, Song, |c (Computer scientist), |e author.
- 245 10 |a Edge learning for distributed big data analytics : |b theory, algorithms, and system design / |c Song Guo, Zhihao Qu.
- 264 _1 |a Cambridge : |b Cambridge University Press, |c 2022.
- 300 __ |a x, 1 unnumbered page, 217 pages : |b illustrations ; |c 25 cm
- 336 __ |a text |b txt |2 rdacontent
- 337 __ |a unmediated |b n |2 rdamedia
- 338 __ |a volume |b nc |2 rdacarrier
- 504 __ |a Includes bibliographical references (unnumbered page 190-page 214) and index.
- 520 __ |a Discover this multi-disciplinary and insightful work, which integrates machine learning, edge computing, and big data. Presents the basics of training machine learning models, key challenges and issues, as well as comprehensive techniques including edge learning algorithms, and system design issues. Describes architectures, frameworks, and key technologies for learning performance, security, and privacy, as well as incentive issues in training/inference at the network edge. Intended to stimulate fruitful discussions, inspire further research ideas, and inform readers from both academia and industry backgrounds. Essential reading for experienced researchers and developers, or for those who are just entering the field.
- 650 _0 |a Edge computing.
- 700 1_ |a Qu, Zhihao, |e author.
- 950 __ |a SCNU |f TP393.027/G977