TY - GEN
T1 - Approximate Classifiers with Controlled Accuracy
AU - Demianiuk, Vitalii
AU - Kogan, Kirill
AU - Nikolenko, Sergey I.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - Performing exact computations can require significant resources. Approximate computing allows to alleviate resource constraints, sacrificing the accuracy of results. In this work, we consider a generalization of the classical packet classification problem. Our major contribution is to introduce various representations for approximate packet classifiers with controlled accuracy and optimization techniques to reduce classifier sizes exploiting this new level of flexibility. We validate our theoretical results with a comprehensive evaluation study.
AB - Performing exact computations can require significant resources. Approximate computing allows to alleviate resource constraints, sacrificing the accuracy of results. In this work, we consider a generalization of the classical packet classification problem. Our major contribution is to introduce various representations for approximate packet classifiers with controlled accuracy and optimization techniques to reduce classifier sizes exploiting this new level of flexibility. We validate our theoretical results with a comprehensive evaluation study.
UR - https://www.scopus.com/pages/publications/85068225520
U2 - 10.1109/INFOCOM.2019.8737476
DO - 10.1109/INFOCOM.2019.8737476
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AN - SCOPUS:85068225520
T3 - Proceedings - IEEE INFOCOM
SP - 2044
EP - 2052
BT - INFOCOM 2019 - IEEE Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Conference on Computer Communications, INFOCOM 2019
Y2 - 29 April 2019 through 2 May 2019
ER -