Authors: Christmann, Andreas
Luebke, Karsten
Marin-Galiano, Marcos
Rüping, Stefan
Title: Determination of hyper-parameters for kernel based classification and regression
Language (ISO): en
Abstract: We investigate methods to determine appropriate choices of the hyper-parameters for kernel based methods. Support vector classification, kernel logistic regression and support vector regression are considered. Grid search, Nelder-Mead algorithm and pattern search algorithm are used.
Subject Headings: convex risk minimization
kernel logistic regression
statistical machine learning
support vector machine
support vector regression
URI: http://hdl.handle.net/2003/21667
http://dx.doi.org/10.17877/DE290R-14494
Issue Date: 2005-11-07T11:39:49Z
Appears in Collections:Sonderforschungsbereich (SFB) 475

Files in This Item:
File Description SizeFormat 
tr38-05.pdfDNB491.21 kBAdobe PDFView/Open


This item is protected by original copyright



All resources in the repository are protected by copyright.