@article{FPerez18,

title = {Complex Gaussian Processes for Regression},

author = {Rafael Boloix-Tortosa and Juan Jos\'{e} Murillo-Fuentes and Francisco Javier Pay\'{a}n-Somet and Fernando P\'{e}rez-Cruz},

doi = {10.1109/TNNLS.2018.2805019},

year  = {2018},

date = {2018-03-06},

journal = {IEEE Transactions on Neural Networks and Learning Systems},

volume = {29},

number = {11},

pages = {5499-5511},

keywords = {Complex-valued processes, Gaussian processes (GPs), kernel methods, regression},

pubstate = {published},

tppubtype = {article}

}

@article{Tuia2011,

title = {Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation},

author = {D Tuia and J Verrelst and L Alonso and Fernando Perez-Cruz and Gustavo Camps-Valls},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5735189},

issn = {1545-598X},

year  = {2011},

date = {2011-01-01},

journal = {IEEE Geoscience and Remote Sensing Letters},

volume = {8},

number = {4},

pages = {804--808},

abstract = {This letter proposes a multioutput support vector regression (M-SVR) method for the simultaneous estimation of different biophysical parameters from remote sensing images. General retrieval problems require multioutput (and potentially nonlinear) regression methods. M-SVR extends the single-output SVR to multiple outputs maintaining the advantages of a sparse and compact solution by using an $epsilon$-insensitive cost function. The proposed M-SVR is evaluated in the estimation of chlorophyll content, leaf area index and fractional vegetation cover from a hyperspectral compact high-resolution imaging spectrometer images. The achieved improvement with respect to the single-output regression approach suggests that M-SVR can be considered a convenient alternative for nonparametric biophysical parameter estimation and model inversion.},

keywords = {Biological system modeling, Biomedical imaging, Biophysical parameter estimation, chlorophyll content estimation, Estimation, fractional vegetation cover, geophysical image processing, hyperspectral compact high-resolution imaging spec, image resolution, leaf area index, model inversion, multioutput support vector regression method, nonparametric biophysical parameter estimation, Parameter estimation, regression, regression analysis, Remote sensing, remote sensing biophysical parameter estimation, remote sensing image, single-output support vector regression method, spectrometers, Support vector machines, support vector regression (SVR), Vegetation mapping},

pubstate = {published},

tppubtype = {article}

}

@article{Perez-Cruz2008c,

title = {Nonlinear Channel Equalization With Gaussian Processes for Regression},

author = {Fernando Perez-Cruz and Juan Jose Murillo-Fuentes and S Caro},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4563433},

issn = {1053-587X},

year  = {2008},

date = {2008-01-01},

journal = {IEEE Transactions on Signal Processing},

volume = {56},

number = {10},

pages = {5283--5286},

abstract = {We propose Gaussian processes for regression (GPR) as a novel nonlinear equalizer for digital communications receivers. GPR's main advantage, compared to previous nonlinear estimation approaches, lies on their capability to optimize the kernel hyperparameters by maximum likelihood, which improves its performance significantly for short training sequences. Besides, GPR can be understood as a nonlinear minimum mean square error estimator, a standard criterion for training equalizers that trades off the inversion of the channel and the amplification of the noise. In the experiment section, we show that the GPR-based equalizer clearly outperforms support vector machine and kernel adaline approaches, exhibiting outstanding results for short training sequences.},

keywords = {Channel estimation, digital communications receivers, equalisers, equalization, Gaussian processes, kernel adaline, least mean squares methods, maximum likelihood estimation, nonlinear channel equalization, nonlinear equalization, nonlinear minimum mean square error estimator, regression, regression analysis, short training sequences, Support vector machines},

pubstate = {published},

tppubtype = {article}

}