@incollection{NIPS2006_710,
	title = {Gaussian and Wishart Hyperkernels},
	address = {Cambridge, MA},
	author = {Risi Kondor and Tony Jebara},
	booktitle = {Advances in Neural Information Processing Systems 19},
	editor = {B. Sch\"{o}lkopf and J. Platt and T. Hoffman},
	publisher = {MIT Press},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/28b801a4b7a0222f3077965b7a1099670/seandalai},
	description = {Most work on kernel learning has focused on finding a kernel which is subsequently to be used in a conventional kernel machine, turning learning into an essentially two-stage process: first learn the kernel, then use it in a conventional algorithm such as an SVM to solve a classification or regression task. Recently there has been increasing interest in using the kernel in its own right to answer relational questions about the dataset . Instead of
predicting individual labels, a kernel characterizes which pairs of labels are likely to be the same, or related. Kernel learning can be used to infer the network structure underlying data.
A dierent application is to use the learnt kernel to produce a low dimensional embedding via kernel PCA. In this sense, kernel learning can be also be regarded as a dimensionality reduction or metric learning algorithm.},
	keywords = {2006 kernels nips }
}