Hardware-based support vector machine classification in logarithmic number systems

Support vector machines are emerging as a powerful machine-learning tool. Logarithmic number systems (LNS) utilize the property of logarithmic compression for numerical operations. We present an implementation of a digital support vector machine (SVM) classifier using LNS in which, when compared wit...

Full description

Saved in:
Bibliographic Details
Main Authors: Khan, F.M., Arnold, M.G., Pottenger, W.M.
Format: Conference Proceeding
Language:English
Subjects:
Application software
Computer science
Event detection
Hardware
Kernel
Machine learning
Neural networks
Power engineering and energy
Support vector machine classification
Support vector machines
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Support vector machines are emerging as a powerful machine-learning tool. Logarithmic number systems (LNS) utilize the property of logarithmic compression for numerical operations. We present an implementation of a digital support vector machine (SVM) classifier using LNS in which, when compared with other implementations, considerable hardware savings are achieved with no significant loss in classification accuracy.
ISSN:0271-4302
2158-1525
DOI:10.1109/ISCAS.2005.1465795