logo
CanyamAI summaries for academic research
language
MobileMobile
  • Phone FeatureWeChat Mini Program
EnglishEnglish
noticeNotice
BlogBlog
delete
arrow
Return

A prediction fusion method for reconstructing spatial temporal dynamics using support vector machines

delete2006-01-01
delete18
PRE
AI
Y
Youshen Xia
H
Henry Leung
DOI：10.1109/TCSII.2005.854585delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
In this paper, we propose a new spatial temporal predictor using support vector machine (SVM) and data fusion technique. SVMs are used as temporal predictors at different spatial domains and spatial temporal prediction is achieved by prediction fusion. Our proposed prediction fusion technique improves the prediction accuracy even in a non-Gaussian environment.,The performance of the proposed spatial temporal predictor is analyzed. Based on real-life radar data, the proposed spatial temporal approach is shown to provide a more accurate model for sea-clutter data than the conventional methods.
Keywords:
data fusion
neural networks
nonlinear dynamics
prediction
signal modeling
spatial temporal dynamics
support vector machine (SVM)
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

I
IEEE Transactions on Circuits and Systems and Express Briefs
IF:
4.9
Papers:
8.8K
Citations:
2.5W

Organization

No organization information available