I heard Kernel Logistic Regression is a classical combination of kernel methods and Logistic regression, but I cannot find any major reference (book, or paper) on this topic. Can you give me any suggestions? Thanks.
I've written a couple ;o)
G. C. Cawley and N. L. C. Talbot, Efficient approximate leave-one-out cross-validation for kernel logistic regression, Machine Learning, vol, 71, no. 2-3, pp. 243--264, June 2008.
Which gives a reasonable method for choosing kernel and regularisation parameters and an empirical evaluation
G. C. Cawley, G. J. Janacek and N. L. C. Talbot, Generalised kernel machines, in Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-2007), pages 1732-1737, Orlando, Florida, USA, August 12-17, 2007.
Which basically documents a MATLAB toolbox for making kernel versions of generalised linear models with kernel logistic regression as one of the examples. The library includes code for model selection (but sadly no manual yet, just some demos)
However the earliest paper I know of that uses that particular name is "Kernel logistic regression and the import vector machine" by Zhu and Hastie, Advances in Neural Information Processing Systems (2001) (available via google scholar)
This is the only reference I know of