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Computing representative learning set via Mathematica
Authors

Béla Paláncz
Organization: Budapest University of Technology and Economics
Department: Photogrammetry and Geoinformatics
Lajos Völgyesi
Organization: Budapest University of Technology and Economics
Department: Department of Geodesy and Surveying
Piroska Zaletnyik
Organization: Budapest University of Technology and Economics
Department: Department of Geodesy and Surveying
Levente Kovacs
Organization: Budapest University of Technology and Economics
Department: Department of Control Engineering and Information Technology
Revision date

2006-11-03
Description

The efficiency of the application of soft computing methods like Artificial Neural Networks (ANN) or Support Vector Machines (SVM) depends considerably on the representativeness of the learning sample set employed for training the model. In this study a simple method based on the Coefficient of Representativity (CR) is proposed for extracting representative learning set from measured geospatial data. The method eliminating successively the sample points having low CR value from the dataset is implemented in Mathematica and its application is illlustrated by the data preparation for the correction model of the Hungarian gravimetrical geoid based on current GPS measurements.
Subject

*Applied Mathematics > Computer Science
Keywords

machine learning, representativness of data, geospatial data
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data01.txt (10 KB) - Text file
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MIC_121329085 .nb (1.6 MB) - Mathematica Notebook
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data02.dat (28.1 KB) - Unknown MIME type