On Thu, May 24, 2012 at 10:35 AM, Mathieu Blondel <[email protected]> wrote: > > On Thu, May 24, 2012 at 11:27 PM, Ian Goodfellow <[email protected]> > wrote: >> >> I think I've figured out what the problem is, but someone familiar >> with the code should confirm. >> I think SVC is always using a decision function based on support >> vectors, even though in the case of a linear kernel it is >> computationally cheaper to just do one dot product in feature space. >> > > Correct. I guess we just assumed that people would use LinearSVC when using > a linear kernel...
Unless something has changed in the last few months, LinearSVC isn't feasible for large, dense, datasets. It forces a copy of the dataset into a sparse matrix format, so you end up using something like 4X the memory of SVC. > > A PR implementing decision_function and predict based on coef_ would be > welcome. > > Mathieu > > ------------------------------------------------------------------------------ > 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
