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StreamSVM is a solver for Linear SVMs that exploits the different speeds of computing on the CPU and accessing data from disk. StreamSVM works by performing coordinate updates on the dual, thus avoiding the need to rebalance frequently visited examples. Further, we trade-off file I/O with data expansion on the fly by generating features on demand, thereby significantly increasing throughput. Experiments show that StreamSVM outperforms other linear SVM solvers by orders of magnitude while also producing more accurate solutions. Joint work with Shin Matsushima and Alex Smola