document: Large linear classification when data can not fit in memory Recent advances in linear classification have shown that for applications such as document classification , the training can be extremely efficient . However , most of the existing training methods are designed by assuming that data can be stored in the computer memory . These methods can not be easily applied to data larger than the memory capacity due to the random access to the disk . We propose and analyze a block minimization framework for data larger than the memory size . At each step a block of data is loaded from the disk and handled by certain learning methods . We investigate two implementations of the proposed framework for primal and dual SVMs , respectively . As data can not fit in memory , many design considerations are very different from those for traditional algorithms . Experiments using data sets 20 times larger than the memory demonstrate the effectiveness of the proposed method .
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