Name
Abstract | ||
|---|---|---|
Consensus theory is adopted as a means of classifying geographic data from multiple sources. The foundations and usefulness of different consensus theoretic methods are discussed in conjunction with pattern recognition. Weight selections for different data sources are considered and modeling of non-Gaussian data is investigated. The application of consensus theory in pattern recognition is tested on two data sets: (1) multisource remote sensing and geographic data, and (2) very-high-dimensional remote sensing data. The results obtained using consensus theoretic methods are found to compare favorably with those obtained using well-known pattern recognition methods. The consensus theoretic methods can be applied in cases where the Gaussian maximum likelihood method cannot. Also, the consensus theoretic methods are computationally less demanding than the Gaussian maximum likelihood method and provide a means for weighting data sources differently |
Year | DOI | Venue |
|---|---|---|
1992 | 10.1109/21.156582 | Systems, Man and Cybernetics, IEEE Transactions |
Keywords | Field | DocType |
geophysical techniques,pattern recognition,remote sensing,consensus theory,geographic data,linear opinion pool,multisource classification,multisource remote sensing,nonGaussian data modelling,pattern recognition,weight selection | Data mining,Knowledge representation and reasoning,Data set,Weighting,Pattern recognition,Computer science,Maximum likelihood,Gaussian,Artificial intelligence,Consensus theory,Machine learning | Journal |
Volume | Issue | ISSN |
22 | 4 | 0018-9472 |
Citations | PageRank | References |
144 | 37.91 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. A. Benediktsson | 1 | 860 | 83.81 |
| P. H. Swain | 2 | 327 | 77.70 |