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Land Cover Classification with Multi-Sensor Fusion of Partly Missing Data
 

Summary: Land Cover Classification with Multi-Sensor Fusion
of Partly Missing Data
We describe how decision tree classifiers can be learned with alternative decision nodes for
handling missing data in multi-source information fusion where one or more measurements
do not exist for some locations.
Authors:
Selim Aksoy
Department of Computer Engineering, Bilkent University, Ankara, 06800, Turkey
saksoy@cs.bilkent.edu.tr
Krzysztof Koperski
Evri, Inc., 206 1st Ave. South, Suite 310, Seattle, WA, 98104, USA
kris@evri.com
(Affiliation at the time the research was performed: Insightful Corporation, 1700 Westlake Ave.
N., Suite 500, Seattle, WA, 98109, USA)
Carsten Tusk
Evri, Inc., 206 1st Ave. South, Suite 310, Seattle, WA, 98104, USA
carsten@evri.com
(Affiliation at the time the research was performed: Insightful Corporation, 1700 Westlake Ave.
N., Suite 500, Seattle, WA, 98109, USA)
Giovanni Marchisio

  

Source: Aksoy, Selim - Department of Computer Engineering, Bilkent University

 

Collections: Computer Technologies and Information Sciences