Retrieval of oceanic chlorophyll concentration with relevance vector machines
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Gustavo Camps-Valls | Javier Calpe-Maravilla | Jordi Muñoz-Marí | Julia Amorós-López | Joan Vila-Francés | Luis Gomez-Chova | L. Gómez-Chova | J. Muñoz-Marí | J. Amorós-López | J. Vila-Francés | J. Calpe-Maravilla | Gustau Camps-Valls
[1] R. Fletcher. Practical Methods of Optimization , 1988 .
[2] H. Zhan,et al. Application of Support Vector Machines in Inverse Problems in Ocean Color Remote Sensing , 2005 .
[3] José F. Moreno,et al. Regularized RBF Networks for Hyperspectral Data Classification , 2004, ICIAR.
[4] Giulietta S. Fargion,et al. Application of machine-learning techniques toward the creation of a consistent and calibrated global chlorophyll concentration baseline dataset using remotely sensed ocean color data , 2003, IEEE Trans. Geosci. Remote. Sens..
[5] J. Amorós-López,et al. Relevance vector machines for sparse learning of biophysical parameters , 2005, SPIE Remote Sensing.
[6] Alexander J. Smola,et al. Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.
[7] José Luis Rojo-Álvarez,et al. Kernel Methods in Bioengineering, Signal And Image Processing , 2007 .
[8] L. Keiner,et al. Estimating oceanic chlorophyll concentrations with neural networks , 1999 .
[9] Lorenzo Bruzzone,et al. Robust multiple estimator systems for the analysis of biophysical parameters from remotely sensed data , 2005, IEEE Transactions on Geoscience and Remote Sensing.
[10] Xiao‐Hai Yan,et al. Development and application of a neural network based ocean colour algorithm in coastal waters , 2005 .
[11] Lorenzo Bruzzone,et al. Kernel-based methods for hyperspectral image classification , 2005, IEEE Transactions on Geoscience and Remote Sensing.
[12] Michael E. Tipping. Sparse Bayesian Learning and the Relevance Vector Machine , 2001, J. Mach. Learn. Res..
[13] Chih-Jen Lin,et al. Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.
[14] Ping Shi,et al. Retrieval of oceanic chlorophyll concentration using support vector machines , 2003, IEEE Trans. Geosci. Remote. Sens..
[15] Lorenzo Bruzzone,et al. Regularized methods for hyperspectral image classification , 2004, SPIE Remote Sensing.
[16] S. Maritorena,et al. Bio-optical properties of oceanic waters: A reappraisal , 2001 .
[17] Michael E. Tipping,et al. Fast Marginal Likelihood Maximisation for Sparse Bayesian Models , 2003 .
[18] James W. Brown,et al. A semianalytic radiance model of ocean color , 1988 .
[19] Christopher Bishop,et al. Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics , 2003 .
[20] Nello Cristianini,et al. An introduction to Support Vector Machines , 2000 .
[21] J. L. Carrasco-Rodriguez,et al. Effective 1-day ahead prediction of hourly surface ozone concentrations in eastern Spain using linear models and neural networks , 2002 .
[22] A. Dyk,et al. Comparison of methods for estimation of Kyoto Protocol products of forests from multitemporal Landsat , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).
[23] M. Kahru,et al. Ocean Color Chlorophyll Algorithms for SEAWIFS , 1998 .
[24] L. Bruzzone,et al. A system for monitoring NO 2 emissions from biomass burning by using GOME and ATSR-2 data , 2003 .
[25] T. M. Lillesand,et al. Remote Sensing and Image Interpretation , 1980 .
[26] R. Courant,et al. Methods of Mathematical Physics , 1962 .
[27] Bernhard Schölkopf,et al. A tutorial on support vector regression , 2004, Stat. Comput..
[28] Lawrence L. Lapin. Probability and Statistics for Modern Engineering , 1983 .
[29] Carl E. Rasmussen,et al. Healing the relevance vector machine through augmentation , 2005, ICML.
[30] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[31] Michael E. Tipping. The Relevance Vector Machine , 1999, NIPS.
[32] Hsuan-Tien Lin. A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods , 2005 .
[33] Lei Ji,et al. Forecasting vegetation greenness with satellite and climate data , 2004, IEEE Geoscience and Remote Sensing Letters.
[34] Marco Diani,et al. Retrieval of sea water optically active parameters from hyperspectral data by means of generalized radial basis function neural networks , 2001, IEEE Trans. Geosci. Remote. Sens..
[35] J. Privette,et al. Inversion methods for physically‐based models , 2000 .
[36] Vladimir Vapnik,et al. Statistical learning theory , 1998 .