2 research outputs found
Incremental learning algorithms and applications
International audienceIncremental learning refers to learning from streaming data, which arrive over time, with limited memory resources and, ideally, without sacrificing model accuracy. This setting fits different application scenarios where lifelong learning is relevant, e.g. due to changing environments , and it offers an elegant scheme for big data processing by means of its sequential treatment. In this contribution, we formalise the concept of incremental learning, we discuss particular challenges which arise in this setting, and we give an overview about popular approaches, its theoretical foundations, and applications which emerged in the last years
Contribuições à modelagem estocástica de algoritmos adaptativos normalizados
Tese (doutorado) - Universidade Federal de Santa Catarina, Centro TecnolĂłgico, Programa de PĂłs-Graduação em Engenharia ElĂ©trica, FlorianĂłpolis, 2015.Este trabalho de pesquisa trata da modelagem estocástica de trĂŞs algoritmos adaptativos bem conhecidos da literatura, a saber: o algoritmo NLMS (normalized least-mean-square), o algoritmo IAF PNLMS (individual-activation-factor proportionate NLMS) e o algoritmo TDLMS (transform-domain least-mean-square). Particularmente para o algoritmo NLMS, um modelo estocástico analĂtico Ă© obtido levando em conta um ambiente nĂŁo estacionário e sinais de entrada gaussianos complexos. Baseado nas expressões de modelo, o impacto dos parâmetros do algoritmo sobre o seu desempenho Ă© discutido, evidenciando algumas das caracterĂsticas de rastreamento do algoritmo NLMS frente ao ambiente nĂŁo estacionário considerado. Para o algoritmo IAF-PNLMS, assumindo um ambiente estacionário, um modelo estocástico mais preciso do que os atĂ© entĂŁo disponĂveis na literatura Ă© apresentado, considerando sinais de entrada gaussianos correlacionados tanto complexos quanto reais. Com respeito ao algoritmo TDLMS, um modelo estocástico melhorado Ă© derivado focando em um ambiente nĂŁo estacionário e sinais de entrada gaussianos correlacionados reais. A partir das expressões de modelo obtidas, o impacto dos parâmetros do algoritmo TDLMS sobre o seu desempenho Ă© discutido. Resultados de simulação para diferentes cenários de operação sĂŁo mostrados, confirmando a precisĂŁo dos modelos estocásticos propostos tanto na fase transitĂłria quanto em regime permanente.Abstract : This research work focuses on the stochastic modeling of three well-known adaptive algorithms from the literature, namely: the normalized least-mean-square (NLMS) algorithm, the individual-activation-factor proportionate NLMS (IAF-PNLMS) algorithm, and the transform-domain least-mean-square (TDLMS) algorithm. Particularly for the NLMS algorithm, an analytical stochastic model is obtained taking into account a nonstationary environment and complex-valued Gaussian input data. Based on the obtained model expressions, the impact of the algorithm parameters on its performance is discussed, clarifying some of the tracking properties of the NLMS algorithm vis-Ă -vis the nonstationary environment considered. For the IAF-PNLMS algorithm, assuming a stationary environment, a more accurate stochastic model than those available so far in the literature is presented considering both complex- and real-valued Gaussian correlated input data. Regarding the TDLMS algorithm, an improved stochastic model is derived focusing on a nonstationary environment and real-valued Gaussian correlated input data. From the obtained model expressions, the impact of the TDLMS algorithm parameters on its performance is discussed. Simulation results for different operating scenarios are shown, confirming the accuracy of the proposed stochastic models for both transient and steady-state phases