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Improvements to Platt's SMO Algorithm for SVM Classifier Design

Neural Computation · 2001 · Vol. 13(3) · pp. 637–649

S. Sathiya Keerthi✉(National University of Singapore)Shirish Shevade(Indian Institute of Science Bangalore)Chiranjib Bhattacharyya(Indian Institute of Science Bangalore)K. R. K. Murthy(Indian Institute of Science Bangalore)

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Abstract

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.

Fuzzy Logic and Control Systems Fault Detection and Control Systems Artificial Intelligence in HealthcareKarush–Kuhn–Tucker conditionsInefficiencySupport vector machineSequential minimal optimizationAlgorithmComputer scienceBenchmark (surveying)Classifier (UML)Artificial intelligenceMachine learning

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