Timeline for Which kernel method gives the best probability outputs?
Current License: CC BY-SA 2.5
5 events
| when toggle format | what | by | license | comment | |
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| Dec 30, 2010 at 12:03 | comment | added | RichardN | Thanks, i will have a closer look at wahba's publications. Can you recommend an implementation of KLR, at best in R? | |
| Dec 30, 2010 at 11:43 | comment | added | Dikran Marsupial | I am not that familiar with the smoothing literature, but kernel models are closely related to spline smoothing. I think the best place to look would be the publications by Grace Wahba (stat.wisc.edu/~wahba), whos work spans both smoothing and kernel methods. | |
| Dec 30, 2010 at 10:59 | comment | added | RichardN | Thank you Dikran! Could you explain to me the relation of KLR und Kernel smoothing? The KLR-model is built similar to the svm [loss + penalty]-formulation and solved via gradient descent. But the same time references (e.g. in "Kernel Logistic Regression and the Import Vector Machine", Zhu and Hastie 2005) on KLR go to the smoothing-literature (e.g. "Generalized Additive Models", Hastie and Tibshirani 1990). | |
| Dec 29, 2010 at 21:16 | comment | added | Dikran Marsupial | I should add, don't use implementations based on the Laplace approximation - the posterior is highly skewed, and a symmetric approximation centered on the mode generally wont work very well. | |
| Dec 28, 2010 at 22:30 | history | answered | Dikran Marsupial | CC BY-SA 2.5 |