Abstract
The performance of active learning (AL) is crucially influenced by the existence of outliers in input samples. In this paper, we propose a robust pool-based AL measure based on the density power divergence. It is known that the density power divergence can be accurately estimated even under the existence of outliers within data. We further derive an AL scheme based on an asymptotic statistical analysis on the M-estimator. The performance of the proposed framework is investigated empirically using artificial and real-world data.
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Sogawa, Y., Ueno, T., Kawahara, Y., Washio, T. (2012). Robust Active Learning for Linear Regression via Density Power Divergence. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7665. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34487-9_72
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DOI: https://doi.org/10.1007/978-3-642-34487-9_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34486-2
Online ISBN: 978-3-642-34487-9
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