Hybrid consensus theoretic classification with pruning and regularization

Jon Atli Benediktsson; Kjartan Benediktsson

Hybrid consensus theoretic classification with pruning and regularization

Jon Atli Benediktsson
, Kjartan Benediktsson

University of Iceland

University of Iceland

Research output: Contribution to conference › Paper › peer-review

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 language	English
Pages	2486-2488
Number of pages	3
Publication status	Published - 1999
Event	Proceedings 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 1999 → 2 Jul 1999

Conference

Conference	Proceedings of the 1999 IEEE International Geoscience and Remote Sensing Symposium (IGARSS'99) 'Remote Sensing of the Systems Earth - A Challenge for the 21st Century'
City	Hamburg, Ger
Period	28/06/99 → 2/07/99

Cite this

@conference{3e18c95b80ee46b9ad718f3c9a15a424,

title = "Hybrid consensus theoretic classification with pruning and regularization",

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.",

author = "Benediktsson, \{Jon Atli\} and Kjartan Benediktsson",

year = "1999",

language = "English",

pages = "2486--2488",

note = "Proceedings of the 1999 IEEE International Geoscience and Remote Sensing Symposium (IGARSS'99) 'Remote Sensing of the Systems Earth - A Challenge for the 21st Century' ; Conference date: 28-06-1999 Through 02-07-1999",

}

Hybrid consensus theoretic classification with pruning and regularization. / Benediktsson, Jon Atli; Benediktsson, Kjartan.
1999. 2486-2488 Paper presented at Proceedings 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.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Hybrid consensus theoretic classification with pruning and regularization

AU - Benediktsson, Jon Atli

AU - Benediktsson, Kjartan

PY - 1999

Y1 - 1999

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/0033327676

M3 - Paper

SP - 2486

EP - 2488

T2 - Proceedings of the 1999 IEEE International Geoscience and Remote Sensing Symposium (IGARSS'99) 'Remote Sensing of the Systems Earth - A Challenge for the 21st Century'

Y2 - 28 June 1999 through 2 July 1999

ER -

Hybrid consensus theoretic classification with pruning and regularization

Abstract

Conference

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