Share Email Print
cover

Proceedings Paper

Information fusion of a large number of sources with support vector machine techniques
Author(s): Jerome J. Braun; Sunil P. Jeswani
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Applications of information fusion include cases that involve a large number of information sources. Methods developed in the context of few information sources may not, and often do not, scale well to cases involving a large number of sources. This paper addresses specifically the problem of information fusion of large number of information sources. Performance of Support Vector Machine (SVM) based approach is investigated in input spaces consisting of thousands of information sources. Microarray pattern recognition, an important bioinformatics task with significant medical diagnostics applications, is considered from the information and sensor data fusion viewpoint, and recognition performance experiments conducted on microarray data are discussed. An approach involving high-dimensional input space partitioning is presented and its efficacy is investigated. The aspects of feature-level and decision-level fusion are discussed as well. The results indicate the feasibility of the SVM based information fusion with large number of information sources.

Paper Details

Date Published: 1 April 2003
PDF: 11 pages
Proc. SPIE 5099, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2003, (1 April 2003); doi: 10.1117/12.486321
Show Author Affiliations
Jerome J. Braun, MIT Lincoln Lab. (United States)
Sunil P. Jeswani, MIT Lincoln Lab. (United States)


Published in SPIE Proceedings Vol. 5099:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2003
Belur V. Dasarathy, Editor(s)

© SPIE. Terms of Use
Back to Top