An Improved Variable Kernel Density Estimator Based on <i>L</i><sub>2</sub> Regularization
The nature of the kernel density estimator (KDE) is to find the underlying probability density function (<i>p.d.f</i>) for a given dataset. The key to training the KDE is to determine the optimal bandwidth or Parzen window. All the data points share a fixed bandwidth (scalar for univaria...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-08-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/9/16/2004 |