Abstract
In this paper, we explore the problem of estimating lower and upper densities from imprecisely defined families of parametric kernels. Such estimations allow to rely on a single bandwidth value, and we show that it provides good results on classification tasks when extending the naive Bayesian classifier.
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Notes
- 1.
A kernel is here a symmetric, non-negative function with \(\int _{\mathbb {R}} K(y)dy=1\) and mean 0.
- 2.
In some sense, to regularize our model.
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Dendievel, G., Destercke, S., Wachalski, P. (2019). Density Estimation with Imprecise Kernels: Application to Classification. In: Destercke, S., Denoeux, T., Gil, M., Grzegorzewski, P., Hryniewicz, O. (eds) Uncertainty Modelling in Data Science. SMPS 2018. Advances in Intelligent Systems and Computing, vol 832. Springer, Cham. https://doi.org/10.1007/978-3-319-97547-4_9
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DOI: https://doi.org/10.1007/978-3-319-97547-4_9
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