StreamSVM: Linear SVMs When Data Does Not Fit In Memory

  • S.V.N. Vishwanathan | Purdue University

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

Speaker Details

I am an Associate professor at Purdue with joint appointments in the departments of Statistics and Computer Science. Prior to coming to Purdue in fall 2008 I was a principal researcher in the Statistical Machine Learning program of NICTA with an adjunct appointment at the College of Engineering and Computer Science, Australian National University. I received my Ph.D in machine learning from the Department of Computer Science and Automation, Indian Institute of Science in the year 2003.

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