Title

Boosting Classification Performance via Data Fusion

Document Type

Conference Proceeding

Publication Date

1-1-2015

Abstract

An engine for fusing data from multiple sensors for classification is provided in this paper. Two novel methods for fusing multiple representations of data with boosting are presented and empirically evaluated against other fusion techniques as candidate algorithms for the fusion engine. We argue that information fusion from sensors operating in complementary regions of the spectrum and/or spatially separated can improve the classification performance.

DOI

10.1109/RADAR.2015.7131102

This document is currently not available here.

Share

COinS