Still assessing the best models/algorithms to use, but primarily unsupervised learning ones. The models will come from 100's of millions of data points. We're looking at learned bayesian networks, predictive analysis, multivariate analysis and clustering approaches over distributed data.
On Tue, 2012-05-08 at 19:53 +0200, Olivier Grisel wrote: > 2012/5/8 Darren Govoni <[email protected]>: > > > > Now, for my problem space, the data models _will not_ fit into memory on > > a single CPU. So there inlies a problem. I suspect, as with most > > engineering solutions, the tradeoff one is confronted with concerns > > resources. One might be willing to trade off time for data size (i.e. it > > will run slower distributed, but you can process larger data sets). > > What kind of models are you dealing with? > ------------------------------------------------------------------------------ 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
