New applications for information fusion and soil moisture forecasting

Authors

A. Khalil
M. K. Gill
Mac McKee, Utah State UniversityFollow

Document Type

Presentation

Journal/Book Title/Conference

In Information Fusion, 8th International Conference. IEEE

Volume

2

Publication Date

7-1-2005

First Page

7

Last Page

7

Abstract

There is much concurrent ongoing research to develop, advance and apply new techniques capable of addressing the diverse applications and complexities of data fusion. In this paper we demonstrate the success of statistical learning theory-based support vector machine (SVM) and sparse Bayesian learning-based relevance vector machine (RVM) to perform reliable predictions. The prognostic capability of SVM and RVM will be utilized to achieve high level inference. The plausibility of these techniques is shown by their superior performance in forecasting soil moisture providing exogenous knowledge.

Recommended Citation

Khalil, A., M. K. Gill, and M. McKee. 2005. New applications for information fusion and soil moisture forecasting. In Information Fusion, July 2005, 8th International Conference (Vol. 2, pp. 7-pp). IEEE.