Hybrid consensus theoretic classification with pruning and regularization

Jon Atli Benediktsson*, Kjartan Benediktsson

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

3 Citations (Scopus)

Abstract

Conventional statistical pattern recognition methods are not appropriate in classification of multisource data since such data cannot, in most cases, be modeled by a common convenient multivariate statistical model. However, methods based on consensus theory have shown potential in classification of multisource data. Here, optimized combination, regularization, and pruning is proposed for consensus theoretic classification. The regularization scheme iteratively adapts regularization parameters by minimizing the validation error.

Original languageEnglish
Pages2486-2488
Number of pages3
Publication statusPublished - 1999
EventProceedings of the 1999 IEEE International Geoscience and Remote Sensing Symposium (IGARSS'99) 'Remote Sensing of the Systems Earth - A Challenge for the 21st Century' - Hamburg, Ger
Duration: 28 Jun 19992 Jul 1999

Conference

ConferenceProceedings of the 1999 IEEE International Geoscience and Remote Sensing Symposium (IGARSS'99) 'Remote Sensing of the Systems Earth - A Challenge for the 21st Century'
CityHamburg, Ger
Period28/06/992/07/99

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