Kernel Regression by Mode Calculation of the Conditional Probability Distribution
| dc.creator | Kuehn, Steffen | |
| dc.date | 2008-11-21 | |
| dc.date.accessioned | 2026-07-07T10:20:04Z | |
| dc.date.available | 2026-07-07T10:20:04Z | |
| dc.description | The most direct way to express arbitrary dependencies in datasets is to estimate the joint distribution and to apply afterwards the argmax-function to obtain the mode of the corresponding conditional distribution. This method is in practice difficult, because it requires a global optimization of a complicated function, the joint distribution by fixed input variables. This article proposes a method for finding global maxima if the joint distribution is modeled by a kernel density estimation. Some experiments show advantages and shortcomings of the resulting regression method in comparison to the standard Nadaraya-Watson regression technique, which approximates the optimum by the expectation value. | |
| dc.description | 11 pages, 5 figures | |
| dc.identifier | https://arxiv.org/abs/0811.3499 | |
| dc.identifier | http://arxiv.org/abs/0811.3499 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/174734 | |
| dc.subject | Machine Learning | |
| dc.title | Kernel Regression by Mode Calculation of the Conditional Probability Distribution | |
| dc.type | text |