Abstract
We present a novel ensemble of logistic linear regressors that combines the robustness of online Bayesian learning with the flexibility of ensembles. The ensemble of classifiers are built on top of a Randomly Varying Coefficient model designed for online regression with the fusion of classifiers done at the level of regression before converting it into a class label using a logistic link function. The locally weighted logistic regressor is compared against the state-of-the-art methods to reveal its excellent generalization performance with low time and space complexities.
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© 2009 Springer-Verlag Berlin Heidelberg
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Edakunni, N.U., Vijayakumar, S. (2009). Efficient Online Classification Using an Ensemble of Bayesian Linear Logistic Regressors. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2009. Lecture Notes in Computer Science, vol 5519. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02326-2_11
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DOI: https://doi.org/10.1007/978-3-642-02326-2_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02325-5
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