Performance Analysis of Kernel Adaptive Filters based on LMS Algorithm

AbstractThe design of adaptive nonlinear filters has sparked a great interest in the machine learning community. The present paper aims to present some recent developments in nonlinear adaptive filtering. It provides an in-depth analysis of the performance and complexity of a class of kernel filters based on the least-mean-squares algorithm. A key feature that underlies kernel algorithms is that they map the data in a high-dimensional feature space where linear filtering is performed. The arithmetic operations are carried out in the initial space via evaluation of inner products between pairs of input patterns called kernels. The SNR improvement and the convergence speed of kernel-based least-mean-squares filters are evaluated on two types of applications: time series prediction and cardiac artifacts extraction from magnetoencephalographic data

Décision et reconnaissance des formes en signal

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Optimizing multilayer networks layer per layer without backpropagation

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Initializing back propagation networks with prototypes

International audienceThis paper addresses the problem of initializing the weights in back propagation networks with one hidden layer. The proposed method relies on the use of reference patterns, or prototypes, and on a transformation which maps each vector in the original feature space onto a unit-length vector in a space with one additional dimension. This scheme applies to pattern recognition tasks, as well as to the approximation of continuous functions. Issues related to the preprocessing of input patterns and to the generation of prototypes are discussed, and an algorithm for building appropriate prototypes in the continuous case is described. Also examined is the relationship between this approach and the theory of radial basis functions. Finally, simulation results are presented, showing that initializing back propagation networks with prototypes generally results in (a) drastic reductions in training time, (b) improved robustness against local minima, and (c) better generalization

On the dimension of the discrete Wigner-Ville transform range space: application to time-frequency based detectors design

International audienceThe information conveyed by the discrete Wigner-Ville representations of real, complex or analytic signals is highly redundant, each time-frequency location being related to others via non-obvious relationships. In this paper, we demonstrate that there also exists a large amount of linear relationships between time-frequency samples. This implies that a whole discrete Wigner-Ville representation can be determined from linear combinations of some selected time-frequency locations. A simple example illustrates this property. Next, we design a linear detector that only exploits the information provided by these locations and that yields the same performance as linear receivers performing in the whole time-frequency domain. Finally, some potential implications of this property are briefly presented

Automatic construction of multilayer networks for non linear regression

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Analyse et traitement du signal: Signaux déterministes et aléatoires, filtrage, estimation

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Production rules generation and refinement in back-propagation networks

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Détermination de l'architecture des réseaux multicouches. Application a un problème de discrimination : l'analyse automatique du sommeil

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Apprentissage de règles de décision à structure imposée et contrôle de la complexité

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