Digital spectral analysis: with applications
Digital spectral analysis: with applications
Simulation of communication systems
Simulation of communication systems
The nature of statistical learning theory
The nature of statistical learning theory
Analog Hardware Implementation of Continuous-Time Adaptive Filter Structures
Analog Integrated Circuits and Signal Processing - Special issue on Learning on Silicon
Optimally smoothed periodogram
Signal Processing
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
The Journal of Machine Learning Research
Kernel independent component analysis
The Journal of Machine Learning Research
A tutorial on support vector regression
Statistics and Computing
Support vector machine techniques for nonlinear equalization
IEEE Transactions on Signal Processing
Support vector method for robust ARMA system identification
IEEE Transactions on Signal Processing
Model optimization of SVM for a fermentation soft sensor
Expert Systems with Applications: An International Journal
Audio based solutions for detecting intruders in wild areas
Signal Processing
Advanced support vector machines for 802.11 indoor location
Signal Processing
A unified SVM framework for signal estimation
Digital Signal Processing
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This paper presents a support vector machines (SVM) framework to deal with linear signal processing (LSP) problems. The approach relies on three basic steps for model building: (1) identifying the suitable base of the Hilbert signal space in the model, (2) using a robust cost function, and (3) minimizing a constrained, regularized functional by means of the method of Lagrange multipliers. Recently, autoregressive moving average (ARMA) system identification and non-parametric spectral analysis have been formulated under this framework. The generalized, yet simple, formulation of SVM LSP problems is particularized here for three different issues: parametric spectral estimation, stability of Infinite Impulse Response filters using the gamma structure, and complex ARMA models for communication applications. The good performance shown on these different domains suggests that other signal processing problems can be stated from this SVM framework.