Le 6 avril 2012 13:37, João André <[email protected]> a écrit : > Dear All, > > Hello. My name is João André and I'm a Portuguese phd student at Oxford > Brookes University. My subject is risk management of bridges during their > construction phase. > I've developed a structural robustness index (which basically weights the > damage accumulation within the structure) which is a random variable but can > only take values between [0,1]. The particularity is that the cdf of this > robustness index is a mixture distribution with non-zero probabilistic > content at R=0 and R=1 and a continuous function within. > The classical polynomial approximation of the response surface fails to give > good results because of these discontinuities. I'm using a community > developed reliability software named OpenTurns (www.openturns.org, developed > by EDF, EADS and PhimecaSoft) to do my analysis and they have implemented > libsvm. However, I believe that within scikits new developments of SVR have > been made that could be more efficient and accurate to solve such a problem > as mine. I kindly ask your opinion about this and I thank you in advance for > your time and attention.
The SVR class is the scikit is just a wrapper on the libsvm implementation so the results will be the same. There is a dense implementation that might be a bit more efficient that the default libsvm by avoiding memory copy but that is not a game changer. For discontinuous target signals you might want to give random forest regression and extremly randomized regression trees a try though: http://scikit-learn.org/dev/modules/ensemble.html -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ For Developers, A Lot Can Happen In A Second. Boundary is the first to Know...and Tell You. Monitor Your Applications in Ultra-Fine Resolution. Try it FREE! http://p.sf.net/sfu/Boundary-d2dvs2 _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
