Which kernel method gives the best probability outputs? Recently I have used Platt's scaling of SVM-outputs to estimate probabilities of default-events. More direct alternatives seem to be "Kernel logistic Regression" (KLR) and the related "Import Vector Machine".
Can anyone tell which kernel method giving probability-outputs is currently state of the art? Does an R-implementation of KLR exist?
Thank you very much for your help!
 A: Gaussian process classification (using Expectation Propagation) is probably the state-of-the-art in machine learning.  There is an excellent book by Rasmussen and Williams (downloadable for free), the website for which has a very good MATLAB implementation.  More software, books, papers etc. here.  However, in practice, KLR will probably work just as well for most problems, the major difficulty is in selecting the kernel and regularisation parameters, which is probably best done by cross-validation, although leave-one-out cross-validation can be approximated very efficiently, see Cawley and Talbot (2008).
A: I guess you know that the kernel for logistic regression is a non parametric one, so first of all you have that restriction.
Regarding the R package the one I know and works pretty well is np: Nonparametric kernel smoothing methods for mixed data types
This package provides a variety of nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. 
Regarding the state of the art kernell I can recomend to experiment with the ones described in this paper from 2009. Read it carefully to choose the one that is best and more actual for you.
