PUBLIC MARKS from ogrisel with tag memory

LASVM is an approximate SVM solver that uses online approximation. It reaches accuracies similar to that of a real SVM after performing a single sequential pass through the training examples. Further benefits can be achieved using selective sampling techniques to choose which example should be considered next. As show in the graph, LASVM requires considerably less memory than a regular SVM solver. This becomes a considerable speed advantage for large training sets. In fact LASVM has been used to train a 10 class SVM classifier with 8 million examples on a single processor.

This is a set of rules of thumb for setting up your PostgreSQL 8.0 server. A lot of the below is based on anecdotal evidence or practical scaling tests; there's a lot about database performance that we, and OSDL, are still working out. However, this should get you started. All information below is useful as of January 12, 2005 and will likely be updated later. Discussions of settings below supercede the recommendations I've made on General Bits.

Jeff Hawkins, Numenta: "Prospects and Problems of Cortical Theory". This is the 10th and final talk given at the Redwood Center for Theoretical Neuroscience