Recurrent radial basis function networks for optimal symbol-by-symbol equalization
Proceedings of the COST #229 international workshop on Adaptive methods and emergent techniques for signal processing and communications
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05
Support vector classifier with hyperbolic tangent penalty function
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
Asymptotic Bayesian decision feedback equalizer using a set of hyperplanes
IEEE Transactions on Signal Processing
Support vector machine techniques for nonlinear equalization
IEEE Transactions on Signal Processing
Support vector machines and the multiple hypothesis test problem
IEEE Transactions on Signal Processing
A practical radial basis function equalizer
IEEE Transactions on Neural Networks
Sample selection via clustering to construct support vector-like classifiers
IEEE Transactions on Neural Networks
Equalization with decision delay estimation using recurrent neural networks
Advances in Engineering Software - Advanced algorithms and architectures for signal processing
Equalization with decision delay estimation using recurrent neural networks
Advances in Engineering Software - Advanced algorithms and architectures for signal processing
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In this paper we apply the structural risk minimization principle as an appropriate criterion to train decision feedback and transversal equalizers. We consider both linear discriminant (optimal hyperplane) and nonlinear discriminant (support vector machine) classifiers as an alternative to the linear minimum mean-square error (MMSE) equalizer and radial basis function (RBF) networks, respectively. A fast and simple adaptive algorithm called the Adatron is applied to obtain the linear or nonlinear classifier. In this way we avoid the high computational cost of quadratic programming. Moreover, the use of soft margin (regularized) classifiers is proposed as a simple way to consider "noisy" channel states: this alternative improves the bit error rate, mainly at low SNR's. Furthermore, an adaptive implementation is discussed. Some simulation examples show the advantages of the proposed linear and nonlinear equalizers: a better performance in comparison to the linear MMSE and a simpler structure in comparison to the RBF (Bayesian).