Support Vector Machines (SVM)

Joseph L. Awange (), Béla Paláncz (), Robert H. Lewis () and Lajos Völgyesi ()
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Joseph L. Awange: Curtin University, Department of Spatial Sciences, School of Earth and Planetary Sciences
Béla Paláncz: Budapest University of Technology and Economics, Department of Geodesy and Surveying, Faculty of Civil Engineering
Robert H. Lewis: Fordham University
Lajos Völgyesi: Budapest University of Technology and Economics, Department of Geodesy and Surveying, Faculty of Civil Engineering

Chapter 11 in Mathematical Geosciences, 2023, pp 391-432 from Springer

Abstract: Abstract The concept of SVM is introduced. The algorithm to compute the optimal hyperplane for classification and implementation is discussed. The characteristics of the different type of kernels are described and illustrated via numerical examples. SVM regression technique, employing insensitive loss function, wavelet and universal Fourier kernels are demonstrated in case of image classification and forecasting of maximum flooding level.

Date: 2023
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DOI: 10.1007/978-3-030-92495-9_11

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