Title
Sorn-Based Cascade Support Vector Machine
Abstract
This paper presents a SORN-based cascade support vector machine (SVM). For the first time a SVM is implemented by the usage of SORNs (Set Of Real Numbers). The SORN representation is a dual number representation that uses a fixed set of exact values and open intervals. Arithmetic operations in SORN representation can be realized by lookup tables which allows fast and low-complexity computing. This arithmetic is used for the non-support vector filtering in the early stages of a cascade SVM. Once the training subsets passed the bottom layer of the cascade, the optimization on the remaining support vectors can be done in classic representations like floating point or fixed point.
Year
DOI
Venue
2020
10.23919/Eusipco47968.2020.9287402
28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020)
Keywords
DocType
ISSN
support vector machine, SVM, unum, SORN, machine learning
Conference
2076-1465
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Nils Hulsmeier100.34
Moritz Bärthel242.00
Jochen Rust33212.51
Steffen Paul414240.96