Kılıç, O. FatihVanlı, N. D.Özkan, H.Delibalta, İ.Kozat, Süleyman Serdar2018-04-122018-04-1220162219-5491http://hdl.handle.net/11693/37739Date of Conference: 29 August-2 September 2016Conference Name: 24th European Signal Processing Conference, EUSIPCO 2016We propose an online algorithm for supervised learning with strong performance guarantees under the empirical zero-one loss. The proposed method adaptively partitions the feature space in a hierarchical manner and generates a powerful finite combination of basic models. This provides algorithm to obtain a strong classification method which enables it to create a linear piecewise classifier model that can work well under highly non-linear complex data. The introduced algorithm also have scalable computational complexity that scales linearly with dimension of the feature space, depth of the partitioning and number of processed data. Through experiments we show that the introduced algorithm outperforms the state-of-the-art ensemble techniques over various well-known machine learning data sets.EnglishArtificial intelligenceLearning algorithmsLearning systemsClassification methodsClassifier modelsEnsemble techniquesOn-line algorithmsOn-line classificationPerformance guaranteesSpace partitioningState of the artSignal processingConference Paper10.1109/EUSIPCO.2016.7760657