Finite Precision Analysis of Support Vector Machine Classification in Logarithmic Number Systems

Finite Precision Analysis of Support Vector Machine Classification in Logarithmic Number Systems (2004)

Cached

Download Links

[www.cse.lehigh.edu]
[www.dimacs.rutgers.edu]

Other Repositories/Bibliography

DBLP

by Faisal M. Khan , Mark G. Arnold , William M. Pottenger

Venue:	Machine Classification in Logarithmic Number Systems”, Proc. of the Euromicro Symposium on Digital Systems Design
Citations:	2 - 1 self

Versions

Version History

Metadata Version 3

User correction supplied by SystemCorrections

Datum	Value	Source
TITLE	Finite Precision Analysis of Support Vector Machine Classification in Logarithmic Number Systems	user correction - Legacy Corrections
AUTHOR NAME	Faisal M. Khan	user correction - Legacy Corrections
AUTHOR AFFIL	Computer Science and Engineering, Lehigh University, Bethlehem	user correction - Legacy Corrections
AUTHOR ADDR	Pennsylvania	user correction - Legacy Corrections
AUTHOR NAME	Mark G. Arnold	user correction - Legacy Corrections
AUTHOR AFFIL	Computer Science and Engineering, Lehigh University, Bethlehem	user correction - Legacy Corrections
AUTHOR ADDR	Pennsylvania	user correction - Legacy Corrections
AUTHOR NAME	William M. Pottenger	user correction - Legacy Corrections
AUTHOR AFFIL	Computer Science and Engineering, Lehigh University, Bethlehem	user correction - Legacy Corrections
AUTHOR ADDR	Pennsylvania	user correction - Legacy Corrections
ABSTRACT	In this paper we present an analysis of the minimal hardware precision required to implement Support Vector Machine (SVM) classification within a Logarithmic Number System architecture. Support Vector Machines are fast emerging as a powerful machine-learning tool for pattern recognition, decision-making and classification. Logarithmic Number Systems (LNS) utilize the property of logarithmic compression for numerical operations. Within the logarithmic domain, multiplication and division can be treated simply as addition or subtraction. Hardware computation of these operations is significantly faster with reduced complexity. Leveraging the inherent properties of LNS, we are able to achieve significant savings over double-precision floating point in an implementation of a SVM classification algorithm.	user correction - Legacy Corrections
YEAR	2004	INFERENCE
VENUE	Machine Classification in Logarithmic Number Systems”, Proc. of the Euromicro Symposium on Digital Systems Design	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	254--261	INFERENCE
CITATIONS	24 found	ParsCit 1.0