Papers by Jose Luis Alvarez
Fuzzy sigmoid kernel for support vector classifiers
Neurocomputing, 2004
In the support vector machines (SVM) framework for pattern recognition, non-linear classifiers re... more In the support vector machines (SVM) framework for pattern recognition, non-linear classifiers require the use of a symmetric and positive semi-definite (PSD) kernel, which transforms the input space samples into a high-dimensional (possibly infinite) feature space, in which a ...
IEEE Signal Processing Letters, 2003
A new approach to the nonparametric spectral estimation on the basis of the support vector method... more A new approach to the nonparametric spectral estimation on the basis of the support vector method (SVM) framework is presented. A reweighted least squared error formulation avoids the computational limitations of quadratic programming. The application to a synthetic example and to a digital communication problem shows the robustness of the SVM spectral analysis algorithm.
IEEE Transactions on Geoscience and Remote Sensing, 2008
Multi-temporal classification of remote sensing images is a challenging problem, in which efficie... more Multi-temporal classification of remote sensing images is a challenging problem, in which efficient combination of different sources of information (e.g. temporal, contextual, or multi-sensor) can improve the results. In this paper, we present a general framework based on kernel methods for the integration of heterogeneous sources of information. Using the theoretical principles in this framework, three main contributions are presented. First, a novel family of kernel-based methods for multi-temporal classification of remote sensing images is presented. The second contribution is the development of non-linear kernel classifiers for the well-known difference and ratioing change detection methods, by formulating them in an adequate high dimensional feature space. Finally, the presented methodology allows the integration of contextual information and multi-sensor images with different levels of nonlinear sophistication. The binary support vector classifier (SVC) and the one-class support vector domain description (SVDD) classifier are evaluated using both linear and non-linear kernel functions. Good
Multi-temporal classification of remote sensing images is a challenging problem, in which efficie... more Multi-temporal classification of remote sensing images is a challenging problem, in which efficient combination of different sources of information (e.g. temporal, contextual, or multi-sensor) can improve the results. In this paper, we present a general framework based on kernel methods for the integration of heterogeneous sources of information. Using the theoretical principles in this framework, three main contributions are presented. First, a novel family of kernel-based methods for multi-temporal classification of remote sensing images is presented. The second contribution is the development of non-linear kernel classifiers for the well-known difference and ratioing change detection methods, by formulating them in an adequate high dimensional feature space. Finally, the presented methodology allows the integration of contextual information and multi-sensor images with different levels of nonlinear sophistication. The binary support vector classifier (SVC) and the one-class support vector domain description (SVDD) classifier are evaluated using both linear and non-linear kernel functions. Good
We introduce two support vector machine (SVM)based approaches for solving antenna problems such a... more We introduce two support vector machine (SVM)based approaches for solving antenna problems such as beamforming, sidelobe suppression, and maximization of the signal-to-noise ratio. A basic introduction to SVM optimization is provided and a complex nonlinear SVM formulation developed to handle antenna array processing in space and time. The new optimization formulation is compared with both the minimum mean square error and the minimum variance distortionless response methods. Several examples are included to show the performance of the new approaches.
IEEE Transactions on Signal Processing, 2004
This paper presents a new approach to auto-regressive and moving average (ARMA) modeling based on... more This paper presents a new approach to auto-regressive and moving average (ARMA) modeling based on the support vector method (SVM) for identification applications. A statistical analysis of the characteristics of the proposed method is carried out. An analytical relationship between residuals and SVM-ARMA coefficients allows the linking of the fundamentals of SVM with several classical system identification methods. Additionally, the effect of outliers can be cancelled. Application examples show the performance of SVM-ARMA algorithm when it is compared with other system identification methods.
IEEE Signal Processing Letters, 2007
Nonlinear system identification based on relevance vector machines (RVMs) has been traditionally ... more Nonlinear system identification based on relevance vector machines (RVMs) has been traditionally addressed by stacking the input and/or output regressors and then performing standard RVM regression. This letter introduces a full family of composite kernels in order to integrate the input and output information in the mapping function efficiently and hence generalize the standard approach. An improved trade-off between accuracy and sparsity is obtained in several benchmark problems. Also, the RVM yields confidence intervals for the predictions, and it is less sensitive to free parameter selection.
IEEE Transactions on Neural Networks, 2006
Nonlinear system identification based on Support Vector Machines (SVM) has been usually addressed... more Nonlinear system identification based on Support Vector Machines (SVM) has been usually addressed by means of the standard SVM regression (SVR), which can be seen as an implicit nonlinear Auto-Regressive and Moving
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Papers by Jose Luis Alvarez