Kari, DariushMarivani, ImanDelibalta, İ.Kozat, Süleyman Serdar2018-04-122018-04-1220162219-5491http://hdl.handle.net/11693/37741Date of Conference: 29 August-2 September 2016Conference Name: 24th European Signal Processing Conference, EUSIPCO 2016We introduce the boosting notion extensively used in different machine learning applications to adaptive signal processing literature and implement several different adaptive filtering algorithms. In this framework, we have several adaptive constituent filters that run in parallel. For each newly received input vector and observation pair, each filter adapts itself based on the performance of the other adaptive filters in the mixture on this current data pair. These relative updates provide the boosting effect such that the filters in the mixture learn a different attribute of the data providing diversity. The outputs of these constituent filters are then combined using adaptive mixture approaches. We provide the computational complexity bounds for the boosted adaptive filters. The introduced methods demonstrate improvement in the performances of conventional adaptive filtering algorithms due to the boosting effect.EnglishAdaptive boostingAdaptive filteringArtificial intelligenceBandpass filtersLearning systemsPiecewise linear techniquesSignal filtering and predictionAdaptive filtering algorithmsAdaptive signal processingBoosting effectsInput vectorMachine learning applicationsPiecewise linearAdaptive filtersConference Paper10.1109/EUSIPCO.2016.7760517