Hey Paolo. Sorry for being unspecific. Using the kernel should be more efficient with higher degree polynomials and when having many features. The dimensionality of the explicit features grows very fast with the degree while the cost of the kernel computation stays the same.
Also SVMs work quite well in may settings. Cheers, Andy ----- Ursprüngliche Mail ----- Von: "Paolo Losi" <[email protected]> An: [email protected] Gesendet: Donnerstag, 9. August 2012 11:53:40 Betreff: Re: [Scikit-learn-general] multivariate regression with higher degree polynomials Hi Andy, On Thu, Aug 9, 2012 at 11:53 AM, Andreas Müller < [email protected] > wrote: Also you might need to normalize the data and set the value of C. Still this should work better than doing the explicit expansion. What do you mean exactly by work better? Paolo ------------------------------------------------------------------------------ Live Security Virtual Conference Exclusive live event will cover all the ways today's security and threat landscape has changed and how IT managers can respond. Discussions will include endpoint security, mobile security and the latest in malware threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ Live Security Virtual Conference Exclusive live event will cover all the ways today's security and threat landscape has changed and how IT managers can respond. Discussions will include endpoint security, mobile security and the latest in malware threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
