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Improvements to Platt's SMO Algorithm for SVM Classifier Design

Department of Mechanical and Production Engineering, National University of Singapore, Singapore-119260

S. K. Shevade

Department of Computer Science and Automation, Indian Institute of Science, Bangalore-560012, India

C. Bhattacharyya

Department of Computer Science and Automation, Indian Institute of Science, Bangalore-560012, India

K. R. K. Murthy

Department of Computer Science and Automation, Indian Institute of Science, Bangalore-560012, India

This article points out an important source of inefficiency in Platt's sequential minimal optimization (SMO) algorithm that is caused by the use of a single threshold value. Using clues from the KKT conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO. These modified algorithms perform significantly faster than the original SMO on all benchmark data sets tried.

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