On Fast Converging Data-Selective Adaptive Filtering
AbstractThe amount of information currently generated in the world has been increasing exponentially, raising the question of whether all acquired data is relevant for the learning algorithm process. If a subset of the data does not bring enough innovation, data-selection strategies can be employed to reduce the computational complexity cost and, in many cases, improve the estimation accuracy. In this paper, we explore some adaptive filtering algorithms whose characteristic features are their fast convergence and data selection. These algorithms incorporate a prescribed data-selection strategy and are compared in distinct applications environments. The simulation results include both synthetic and real data. View Full-Text
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Mendonça, M.O.K.; Ferreira, J.O.; Tsinos, C.G.; Diniz, P.S.R.; Ferreira, T.N. On Fast Converging Data-Selective Adaptive Filtering. Algorithms 2019, 12, 4.
Mendonça MOK, Ferreira JO, Tsinos CG, Diniz PSR, Ferreira TN. On Fast Converging Data-Selective Adaptive Filtering. Algorithms. 2019; 12(1):4.Chicago/Turabian Style
Mendonça, Marcele O. K.; Ferreira, Jonathas O.; Tsinos, Christos G.; Diniz, Paulo S R; Ferreira, Tadeu N. 2019. "On Fast Converging Data-Selective Adaptive Filtering." Algorithms 12, no. 1: 4.
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