Online Ensemble Learning

By Nikunj C. Oza. 2000

1,007 citations recorded by the source corpus.

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Citations in the corpus

Visual tracking with online Multiple Instance Learning (2009, methodology): “Nevertheless, we adopt the loss function in Equation 4 and the bag probability model in Equation 5 when we develop our online MIL algorithm in Our algorithm for online MIL is based on the boosting framework [11] and is related to the work on Online AdaBoost [20] and its adaptation in [14].”
Classifier Ensembles for Changing Environments (2004, methodology): “Oza proposes a neat online bagging algorithm which converges to the batch bagging as the number of training examples and the number of classifiers tend to infinity [19].”
Correlation filters with limited boundaries (2014, background): “detection/tracking through convolution) are performed in the spatial domain [1], [2], [16], [18].”
Incremental Learning of Concept Drift in Nonstationary Environments (2011, methodology): “Kuncheva puts ensemble-based approaches into one of the three general categories [15]: given new data, those that: 1) update the combination rules or voting weights of a fixed ensemble, such as [37], [38]; the origins of which can be traced to Littlestone’s Winnow [39] and Freund and Schapire’s Hedg”
A framework for generating data to simulate changing environments (2007, background): “KEY WORDS Machine Learning, Changing environments, Concept drift, Population drift, Artificial data, Simulated data.”
AdaBoost-Based Algorithm for Network Intrusion Detection (2008, methodology): “2) We will investigate the application of online versions [55], [56] of boosting algorithms to incremental learning for network”