SemiSVR

The programs are granted free of charge for research and education purposes only. Scientific results produced using the software provided shall acknowledge the use of the SemiSVR implementation provided by us. If you plan to use it for non-scientific purposes, don't hesitate to contact us.

Because the programs are licensed free of charge, there is no warranty for the program, to the extent permitted by applicable law. except when otherwise stated in writing the copyright holders and/or other parties provide the program "as is" without warranty of any kind, either expressed or implied, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose. the entire risk as to the quality and performance of the program is with you. should the program prove defective, you assume the cost of all necessary servicing, repair or correction.

In no event unless required by applicable law or agreed to in writing will any copyright holder, or any other party who may modify and/or redistribute the program, be liable to you for damages, including any general, special, incidental or consequential damages arising out of the use or inability to use the program (including but not limited to loss of data or data being rendered inaccurate or losses sustained by you or third parties or a failure of the program to operate with any other programs), even if such holder or other party has been advised of the possibility of such damages.

SemiSVR [Semi-supervised Support Vector Regression]
Authors	G. Camps-Valls and J. Muñoz-Marí
Download	Full Matlab Package
Code for the paper (please cite this paper!)	"Biophysical Parameter Estimation with a Semi-supervised Support Vector Machine" Gustavo Camps-Valls, Jordi Muñoz-Marí, Luis Gómez-Chova, Katja Richter and Javier Calpe-Maravilla IEEE Geoscience and Remote Sensing Letters, 6(2), 248-253, April 2009
Abstract	This letter presents two kernel-based methods for semisupervised regression. The methods rely on building a graph or hypergraph Laplacian with both the available labeled and unlabeled data, which is further used to deform the training kernel matrix. The deformed kernel is then used for support vector regression (SVR). Given the high computational burden involved, we present two alternative formulations based on the Nystrom method and the incomplete Cholesky factorization to achieve operational processing times. The semisupervised SVR algorithms are successfully tested in multiplatform leaf area index estimation and oceanic chlorophyll concentration prediction. Experiments are carried out with both multispectral and hyperspectral data, demonstrating good generalization capabilities when a low number of labeled samples are available, which is usually the case in biophysical parameter retrieval.

Copyright & Disclaimer	The programs are granted free of charge for research and education purposes only. Scientific results produced using the software provided shall acknowledge the use of the SemiSVR implementation provided by us. If you plan to use it for non-scientific purposes, don't hesitate to contact us. Because the programs are licensed free of charge, there is no warranty for the program, to the extent permitted by applicable law. except when otherwise stated in writing the copyright holders and/or other parties provide the program "as is" without warranty of any kind, either expressed or implied, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose. the entire risk as to the quality and performance of the program is with you. should the program prove defective, you assume the cost of all necessary servicing, repair or correction. In no event unless required by applicable law or agreed to in writing will any copyright holder, or any other party who may modify and/or redistribute the program, be liable to you for damages, including any general, special, incidental or consequential damages arising out of the use or inability to use the program (including but not limited to loss of data or data being rendered inaccurate or losses sustained by you or third parties or a failure of the program to operate with any other programs), even if such holder or other party has been advised of the possibility of such damages.