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A Randomized Algorithm for Large Scale Support Vector Learning
Krishnan S Kumar · Chiranjib Bhattacharyya · Ramesh Hariharan

Tue Dec 04 10:30 AM -- 10:40 AM (PST) @

We propose a randomized algorithm for large scale SVM learning which solves the problem by iterating over random subsets of the data. Crucial to the algorithm for scalability is the size of the subsets chosen. In the context of text classification we show that, by using ideas from random projections, a sample size of O(log n) can be used to obtain a solution which is close to the optimal with a high probability. Experiments done on synthetic and real life data sets demonstrate that the algorithm scales up SVM learners, without loss in accuracy.

Author Information

Krishnan S Kumar (Indian Institute of Science)
Chiranjib Bhattacharyya (Indian Institute of Science)
Ramesh Hariharan

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