Simplifying Support Vector Machines for Regression analysis of hyperspectral imagery
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[1] B. Schölkopf,et al. Asymptotically Optimal Choice of ε-Loss for Support Vector Machines , 1998 .
[2] Luis Guanter,et al. Simulation of Optical Remote-Sensing Scenes With Application to the EnMAP Hyperspectral Mission , 2009, IEEE Transactions on Geoscience and Remote Sensing.
[3] W. Verhoef,et al. Coupled soil–leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data , 2007 .
[4] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[5] Benyang Tang,et al. Spacebased Estimation of Moisture Transport in Marine Atmosphere Using Support Vector Regression , 2008 .
[6] Vladimir Vapnik,et al. The Nature of Statistical Learning , 1995 .
[7] José Luis Rojo-Álvarez,et al. Robust support vector regression for biophysical variable estimation from remotely sensed images , 2006, IEEE Geoscience and Remote Sensing Letters.
[8] Jeffrey T. Walton. Subpixel urban land cover estimation: comparing cubist, random forests, and support vector regression , 2008 .
[9] Jian Liu,et al. Transformation model estimation of image registration via least square support vector machines , 2006, Pattern Recognit. Lett..
[10] Federico Girosi,et al. Reducing the run-time complexity of Support Vector Machines , 1999 .
[11] S. Durbha,et al. Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer , 2007 .
[12] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.