In article <[EMAIL PROTECTED]>, Rajarshi Guha <[EMAIL PROTECTED]> writes >Hi, > I've come across kernel density estimation technques and I was wonderin >what can they be used for? It appears to me that they just give a >representation of the PDF for the given data. But can these technique be >used for other purposes?
KDEs are often used for classification. > >Another related question is that I have seen some examples of 1D and 2D >KDE technqiues - is it possible (or rather available) to have nD KDE >techniques? Yes. For n > 1 the shape of the kernel is important as well as the size. Most often, the kernel is gaussian, like a multivariate normal, so you specify a covariance matrix to describe the kernel. > >I'd appreciate it if anybody could point some introductory texts in this >area ? > I'm not sure if it is introductory, but Fukunaga, 'Statistical Pattern Recognition', covers KDEs for classification. They're also known as Parzen density estimates. -- Graham Jones http://www.visiv.co.uk Emails to [EMAIL PROTECTED] may be deleted as spam Please add a j just before the @ to ensure delivery . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
