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Computing representative learning set via Mathematica
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| Organization: | Budapest University of Technology and Economics |
| Department: | Photogrammetry and Geoinformatics |
| Organization: | Budapest University of Technology and Economics |
| Department: | Department of Geodesy and Surveying |
| Organization: | Budapest University of Technology and Economics |
| Department: | Department of Geodesy and Surveying |
| Organization: | Budapest University of Technology and Economics |
| Department: | Department of Control Engineering and Information Technology |
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2006-11-03
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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.
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machine learning, representativness of data, geospatial data
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| data01.txt (10 KB) - Text file | | MIC_121329085 .nb (1.6 MB) - Mathematica Notebook | | data02.dat (28.1 KB) - Unknown MIME type |
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