BibSonomy publications for /author/De+Risi/2006
http://www.bibsonomy.org/author/De+Risi/2006
BibSonomy RSS Feed for /author/De+Risi/2006Gaussian and Wishart HyperkernelsMost 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 di erent 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.
http://www.bibsonomy.org/bibtex/28b801a4b7a0222f3077965b7a1099670/seandalai
seandalai2007-01-29T15:55:00+01:00
2006 kernels nips